two point hospital research 3

Research in Two Point Hospital is one of the optional rooms. Patients will go there at each stage of diagnosis to get more information.

A Doctor with Research should be hired to run the Research. You can train a Doctor with Research , Research II , Research III , Research IV , to let the Doctor work in this Room. The more he is trained the faster your research will finish. For treatment, the hospital needs a Research.

Costs to build [ ]

Minimal equipment [ ].

Two Point Hospital Research Guide

After you progress through the first few levels you will eventually unlock the ability to build a Research Lab. From this point forward you can hire Researchers to Research new and valuable technology for your hospital. Research is shared across all hospitals so you can pick up and drop research whenever you feel like it. However, some of it takes a while so it’s a good idea to get it out the way.

Once you have both rooms setup, select each one individually and change the extra staff option to maximum, which is 4. Then you need to either hire or train 8 doctors, 4 for each research room, in research. Check out our Training Guide for more information. Once you have your research team ready, research the new Research trait. This will allow your team to learn Research II, Research III and so forth. This greatly expedites the whole research process.

To further improve it, click an employee and click the job option on the lower right.

This will bring up one of Two Point Hospitals greatest features. From here you can assign employees to certain tasks, or forbid them from working in certain areas. From here you want to ensure your Research team ONLY works in research. Employ other people to fill gaps, let your researchers do their thing.

This will allow you to research quickly and efficiently, without having to build a lot of research stations later in the game. If you have any questions about out Two Point Hospital Research Guide , post a comment below.

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Two Point Hospital Research Centre: How to Research New Rooms

By Tom Power

Two Point Hospital , the spiritual successor to Theme Hospital , is out now. As part of maintaining each hospital to a high standard, players are tasked with building a Two Point Hospital Research Centre to build new diagnostic, surgical and treatment rooms. These new sections in the hospitals will be able to treat and cure patients of their various diseases. Just how do Two Point Hospital players go about researching these necessary rooms though?

How do you Build a  Two Point Hospital Research Centre?

Before you can build a research centre, you need to unlock Mitton University in Two Point Hospital . Mitton University becomes available after you achieve a one-star rating on previous hospital Flottering. A Two Point Hospital Research Centre requires you to train a doctor – the only staff doctor you can hire here, mind you – in research to be able to use their talents in a research centre.

Once your doctor is trained in research, you can put them in a pre-built research room and get them to work on launching new research projects, become better at diagnosing Two Point Hospital ‘s ailments and discover new rooms to build in your hospitals.

Two Point Hospital Research Centre – How to Research New Rooms

Once your Two Point Hospital Research Centre is built, you can drop a doctor with a research qualification into the centre and get to work. First, you need to select a project to research from the available projects list, which should include the option of new rooms to build. Once selected, you need to green light the project – which costs money – and the trained doctor will put their brain to use in figuring out to make this new room a reality.

You can increase how quickly new rooms are researched by increasing the number of trained research doctors in the same centre. Alternatively, you can share your research across the number of Two Point Hospital s you own, and this will help ramp up how rapid new rooms are researched. Once they are, it’s a matter of having enough room in an available hospital to build them.

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Level 3 R.E.M.I.X - Flottering

Two point hospital  — strategy guide (pc).

Strategy Guide (PC) by CityBuilderAK47

Version: 1.9 | Updated: 04/10/2023 FAQ of the Month Winner: October 2018

• Previous: Level 3: Flottering

Table of Contents

• Next: Cold Region
• Intro and Author's Latest Notes
• About Two Point Hospital
• PC Version vs. Console Versions
• Starting Off
• Theme Hospital vs. Two Point Hospital
• Important Game Concepts
• 11 Tips for Hospital Success
• The 9 General Rules of Hospital Layout
• Which Kudosh Items Do You Recommend First?
• Before You Build: Staffing
• Staff Happiness & Staff Threats
• Doctor Skills
• Nurse Skills
• Assistant Skills
• Janitor Skills
• Common Skills
• Staff Traits
• Staff Attire
• Training Tips
• Building a Better Hospital
• Hospital Level Snowballing
• Expansion Strategy
• Examples in Efficiency
• Room Prestige
• Patient Happiness
• The Great Patient Purge
• Level Guides

Countryside Region

• Level 1: Hogsport
• Level 1 R.E.M.I.X - Hogsport
• Level 2: Lower Bullocks
• Level 2 R.E.M.I.X - Lower Bullocks
• Level 3: Flottering
• Cold Region
• Level 4: Mitton University
• Level 4 R.E.M.I.X : Mitton University
• Level 5: Tumble
• Level 5: Tumble - Case Study - Improving the Hospital From 2 to 3 Stars
• Level 5 R.E.M.I.X: Tumble
• Level 6: Flemington
• Level 6 R.E.M.I.X: Flemington
• Level 6: Flemington - Case Study - 2 Stars to 3 Stars
• Level 6: Flemington - 3-Star Winning Layout
• Industrial Region
• Level 7: Smogley
• Level 7: Smogley - Case Study - From Failing 1 Star to Succesful 3 Star
• Level 7: Smogley - 3 Star Winning Layout
• Level 8: Melt Downs
• Level 8: Melt Downs - 3-Star Winning Layout
• Level 9: Duckworth-Upon-Bilge
• Level 9: Duckworth-upon-Bilge - 3-Star Winning Layout
• A Good Resting Point?
• Tropical Region
• Level 10: Sweaty Palms
• Level 10: Sweaty Palms - Case Study - A Failing Hospital (When to Give Up)
• Level 10: Sweaty Palms - 3-Star Winning Layout
• Level 11: Grockle Bay
• Level 11: Grockle Bay - 3-Star Winning Layout
• Level 12: Blighton
• Level 12: Blighton - 3-Star Winning Layout
• Urban Region
• Level 13: Rotting Hill
• Level 13: Rotting Hill - 3-Star Winning Layout
• Level 14: Pelican Wharf
• Level 14: Pelican Wharf - 3-Star Winning Layout
• Level 15: Croquembouche
• Level 15: Croquembouche - 3-Star Winning Layout
• (Superbug Initiative) - The Superbug Initiative
• (Superbug Initiative) - Research Projects
• (Superbug Initiative) - Project-Unlocked Items
• Common 2-Star Challenges
• Staff Morale? Are You Kidding Me?
• Money Troubles
• Common 3-Star Challenges
• You want HOW Much in Hospital Value?
• Attractiveness Rating
• Bump that Cure Rate Up!
• Wave / Horde Gameplay Changes
• Sandbox (PC Only)
• Interior Designer (PC Only)
• Room Templates
• Staff Challenges
• List of Illnesses
• Chance of Cure
• Rogue Monobrows / Monobeasts
• Emergencies
• Item Listing
• Kudosh Item Listing
• (Special) Item Listing
• (Retro Items Pack) DLC Item Listing
• (Exhibition Items Pack) DLC Item Listing
• (Fancy Dress Pack) DLC Item Listing
• (SEGA 60th Anniversary DLC) Sonic-Themed Item Listing
• (Two Point Campus Crossover Items) Two Point Campus themed Items & Costumes
• Research Projects
• End of Year Awards
• Steam Achievement Guide
• Additional Reading
• Additional Resources
• Questions from Parents
• Where Your Save Files Are Stored (PC Only)
• Updates / Additional Content
• Contact the Author
• Legal Bla Bla Bla

R.E.M.I.X Level Description: Profit from career development (not yours). R.E.M.I.X Level Starting Blurb: In this scenario, R.E.M.I X has brutally diminished all of your usual forms of income, including diagnosis and treatment. But don't worry - I can tell that you were worried, isn't that weird? - because you'll receive ample monetary rewards for completing challenges, or "Side Quests", related to staff development, as well as for training your staff.

Awards Available: Rising Star, Employer of the Year, No Deaths, Best Teaching Hospital, Patients' Choice Award, Most Prestigious Hospital, Best Research Hospital, Hospital of the Year Disasters Seen: Disasters do not appear in the R.E.M.I.X Countryside region. Online Challenges Available : There are no online challenges available for this R.E.M.I.X level. Illnesses Seen: Bed Face, Boggled Mind, Bogwarts, Freudian Lips, Headcrabedness, Inflated Ego, Jazz Hand, Jest Infection, Lazy Bones, Lightheadedness, Lycanthropy, Mime Crisis, Misery Guts, Mock Star, Monobrow, Mucky Feet, Pandemic, Portishead, Potty Mouth, Verbal Diarrhoea Most Frequently Seen Illnesses: Pandemic, Bogwarts, Jest Infection, Misery Guts, Mime Crisis Blue Star Ratings: S for Staggering (Completed by Year 5, Hospital Value of nearly $1.9 million, 240 patients cured) Your challenge:

• Train 20 Staff
• Train 3 Staff in Treatment III
• Cure Rate of 70%

So this is a fun twist on how you'd normally play. Basically, your normal income streams are nerfed (not completely, but a good deal). This challenge is definitely for a hospital that specializes in training. You'll receive an $8,000 bonus each time a class completes training, and you'll receive a significant boost of REP and money when you meet one of the various training-related "Side Quests". These pop up periodically much like the targets do in Duckworth-Upon-Bilge . However, they are fairly predictable, and given the emphasis on training in this level, you'll passively be unlocking most of them anyways. These goals typically include:

• Train a Staff Member
• Train a Doctor
• Train a Nurse
• Train an Assistant
• Train a Janitor
• Promote a Staff Member

So what would you do different for this level? Well, at first your hospital may be a bit strapped for cash, but make sure that training rooms are a definite priority. Similar to Swelbard (Bigfoot DLC), you'll get your pick from basically entry-level positions, and this allows you to make your staff what you want. Be sure to select for positive traits and train them the exact way you want! You'll find yourself being a cheapskate in this level, so typically I just aim for Prestige Level 3 for my rooms, and run with it. As long as you keep training your staff (there will reach a point where you basically have a constant stream of staff ready to be trained), you should be able to keep your head above water and do well. Don't be afraid to use the Guest Trainers if training a staff member with another one might impede your normal hospital operations - remember you're basically running at reduced normal cost and relying on stipends for training, so you still want to keep your hospital able to run!

• Open access
• Published: 25 June 2024

Healthcare-associated infections and antimicrobial use in acute care hospitals: a point prevalence survey in Lombardy, Italy, in 2022

• Antonio Antonelli 1 ,
• Maria Elena Ales 1 ,
• Greta Chiecca 1 ,
• Zeno Dalla Valle 1 ,
• Emanuele De Ponti 1 ,
• Danilo Cereda 2 ,
• Lucia Crottogini 2 ,
• Cristina Renzi 1 , 4 ,
• Carlo Signorelli 1 &
• Matteo Moro 3  

BMC Infectious Diseases volume  24 , Article number:  632 ( 2024 ) Cite this article

145 Accesses

Metrics details

Healthcare-Associated Infections (HAIs) are a global public health issue, representing a significant burden of disease that leads to prolonged hospital stays, inappropriate use of antimicrobial drugs, intricately linked to the development of resistant microorganisms, and higher costs for healthcare systems. The study aimed to measure the prevalence of HAIs, the use of antimicrobials, and assess healthcare- and patient-related risk factors, to help identify key intervention points for effectively reducing the burden of HAIs.

A total of 28 acute care hospitals in the Lombardy region, Northern Italy, participated in the third European Point Prevalence Survey (PPS-3) coordinated by ECDC for the surveillance of HAIs in acute care hospitals (Protocol 6.0).

HAIs were detected in 1,259 (10.1%, 95% CI 9.6–10.7%) out of 12,412 enrolled patients. 1,385 HAIs were reported (1.1 HAIs per patient on average). The most common types of HAIs were bloodstream infections (262 cases, 18.9%), urinary tract infections (237, 17.1%), SARS-CoV-2 infections (236, 17.0%), pneumonia and lower respiratory tract infections (231, 16.7%), and surgical site infections (152, 11.0%). Excluding SARS-CoV-2 infections, the overall prevalence of HAIs was 8.4% (95% CI 7.9–8.9%). HAIs were significantly more frequent in patients hospitalized in smaller hospitals and in intensive care units (ICUs), among males, advanced age, severe clinical condition and in patients using invasive medical devices. Overall, 5,225 patients (42.1%, 95% CI 41.3–43.0%) received systemic antimicrobial therapy. According to the WHO’s AWaRe classification, the Access group accounted for 32.7% of total antibiotic consumption, while Watch and Reserve classes accounted for 57.0% and 5.9% respectively. From a microbiological perspective, investigations were conducted on only 64% of the HAIs, showing, however, a significant pattern of antibiotic resistance.

Conclusions

The PPS-3 in Lombardy, involving data collection on HAIs and antimicrobial use in acute care hospitals, highlights the crucial need for a structured framework serving both as a valuable benchmark for individual hospitals and as a foundation to effectively channel interventions to the most critical areas, prioritizing future regional health policies to reduce the burden of HAIs.

Peer Review reports

Introduction

Healthcare-Associated Infections (HAIs) are a global public health issue, representing a significant burden of disease, suffering and mortality [ 1 ]. HAIs may lead to prolonged hospital stays [ 2 ], resulting in considerable costs for healthcare systems [ 3 , 4 , 5 ]; additionally, they are associated with an increased risk of inappropriate use of antimicrobial drugs and the development of resistant microorganisms [ 6 ], against which there will progressively be fewer and less effective antimicrobial drugs [ 7 ].

Literature highlights compelling evidence that the burden of HAIs can be mitigated through appropriate interventions [ 8 ]. However, despite the efforts [ 9 ], according to the most recent estimates from the second European Point Prevalence Survey held between 2016 and 2017, in Europe 8.9 million HAI occurred and 3.8 million patients experienced at least one HAI [ 10 ].

The surveillance of HAIs and of antimicrobial use is essential at hospital, regional, national, and international levels for providing a structured benchmarking framework and for informing appropriate and coordinated health policies [ 11 , 12 , 13 ]. The ECDC Point Prevalence Survey of healthcare-associated infections and antimicrobial use in European acute care hospitals (PPS) is a standardized data collection framework conducted every five years in the 27 EU/EEA countries, plus the UK and Serbia. The first PPS was carried out between 2011 and 2012 [ 14 , 15 ], the second (PPS-2) between 2016 and 2017 [ 16 ] and the third and most recent one (PPS-3) between 2022 and 2023 [ 17 ].

In Italy, PPS-3 was nationally coordinated by the University of Turin, with data collection decentralized in each of the participating regions [ 18 ]. In Lombardy, the most densely populated region in Italy, 28 Acute Care Hospitals (ACHs) voluntarily participated. This study aims to examine the PPS-3 data collected in the Lombardy region between November and December 2022. In particular, we evaluated the prevalence of HAIs, the use of antimicrobials, as well as assessing healthcare-related factors and patient characteristics.

Study design and data collection

The survey was conducted following the ECDC Protocol 6.0 [ 17 ]. Each participating ACH submitted all the data regarding the hospital organization, the use of antimicrobials and the HAIs to a national data repository named RedCap [ 19 , 20 ].

Data collection took place between November 3 and December 20, 2022, with each ward conducting data collection on a specific day. Access to the RedCap platform was granted to data entry operators following a training session on GDPR and data protection. This platform was also accessible to regional coordinators from the Welfare General Directorate of Lombardy Region, enabling them to access data for all hospitals in the region.

According to the protocol [ 17 ], all patients in the eligible wards were included and both hospital and patient data were anonymized during analysis.

ACHs were classified based on capacity in small (≤ 200), medium-size (201–499) and large (≥ 500 beds). Data were collected from the wards for each eligible patient, encompassing risk factors, the presence of HAIs and the use of at least one antimicrobic (grouped using the WHO AWaRe classification [ 21 ], when applicable); the protocol defines HAIs as active if symptoms occur on day 3 or later of the current admission, with specific exceptions regarding the timeframe to be considered in case of surgical site infections (SSI), infections related to an invasive medical device, C. difficile infections, and if the patient has been readmitted within 48 h.

Statistical analysis

Initially, a coherence analysis was performed to identify records with logical inconsistencies resulting from errors during the form submission. A total of 14 flags were identified, and corrective actions were taken for each (details in the Supplementary Table 1 ).

Descriptive analyses included the median and Interquartile range (IQR) for continuous variables, and frequency distribution of categorical variables. The prevalence of HAIs, computed as the proportion of patients with at least one HAI, and antimicrobial use were stratified by epidemiologically significant variables according to previous ECDC report [ 16 ]. Confidence intervals were computed using the Clopper-Pearson exact method for proportions. We employed chi-square tests for evaluating whether the prevalence of HAIs and antibiotic use differed by healthcare- and patient-related factors.

Data on pan-drug resistant microorganism were cross verified with data available in the regional microbiological surveillance system for confirmation.

Data were analyzed using STATA version 18.0 (StataCorp. 2023. Stata Statistical Software: Release 18 College Station, TX: StataCorp LLC) and Python version 3.10.9 with the pandas library version 1.5.3.

Data were collected from 12,412 patients across 28 ACHs, comprising 39 acute care facilities throughout the Lombardy Region. Each hospital enrolled a median of 434 patients (IQR: 199–663). Participating facilities constituted 20% (39 out of 195) of all acute care facilities and accounted for 44% (18,620 out of 42,018) of acute care beds within the Region. Additionally, these facilities (5 small, 7 medium-sized, and 16 large hospitals) represented 50% (646,261 out of 1,288,198) of annual hospital admissions.

Patients’ characteristics

Out of 12,412 patients enrolled in the study, 6,465 (52.2%) were male, 5,930 (47.8%) were female while the sex of 7 patients was unspecified. The median age was 68 years (IQR: 48–79, minimum 0, maximum 103). 740 patients were younger than 2 years, as infants were also included in the study.

Most enrolled patients were admitted to Medicine (14.7%), General Surgery (6.8%), and Cardiology (6.1%) wards. Other specialized wards each accounted for less than 5% of admissions.

Based on the estimated clinical severity assessed using the McCabe Score, 67.2% of the enrolled patients had a non-fatal disease (expected survival > 5 years), 16.6% had an ultimately fatal disease (expected survival 1 to 5 years), 6.6% had a rapidly fatal disease (expected survival < 1 year), and in 9.6% of cases the McCabe Score was unknown or unregistered.

Of the enrolled patients, 34% (4,278) underwent surgery on the day of the study, and among them 2,783 (22%) underwent major surgery, and 1,495 (12%) underwent a minimally invasive surgery.

Use of invasive medical devices

4,548 (36.6%) patients had at least one invasive medical device (MD) in place (urinary catheter, central venous catheter, and/or intubation), specifically 3,468 (76.3%) had only one device, 766 (16.8%) had two, and 314 (6.9%) had three MDs.

The most used MD was the urinary catheter, 3,582 (29.1%) patients, followed by the central venous catheter (1,919, 15.5%) and intubation (441, 3.6%).

The number and type of MDs varied by care area, with the highest utilization observed in the intensive care units (71.9% of patients using at least one device), followed by medical (43.3%) and surgical (39.2%) wards.

Prevalence of HAIs

Healthcare-associated infections were detected in 1,259 patients, resulting in a prevalence of 10.1% (95% CI 9.6–10.7%). In total, 1,385 HAIs were reported, with 1.1 HAIs per patient on average.

Among the participating ACHs, the prevalence of HAIs varied significantly, ranging from 1.4% (95% CI 0.2-5.0%) to 28.2% (95% CI 18.6–39.5%).

Specifically, 260 (2.1%) patients had at least one HAI upon hospital admission, while 998 (8.0%) patients developed the HAI during their stay in the hospital. Excluding hospital-acquired SARS-CoV-2 infections from the analysis, the overall prevalence of HAIs was 8.4% (95% CI 7.9–8.9%), affecting 1,045 patients. Of these, 795 (6.4%) developed at least one HAI during their hospitalization and 247 (2.0%) had an HAI present upon admission.

As shown in Table  1 , the prevalence of HAIs was significantly higher in men, in patients aged over 64 years, in those with severe McCabe score or having undergone major surgery. When stratifying by hospital size, a higher prevalence of HAIs was observed in small and large hospitals compared to medium-sized hospitals. Further stratification by healthcare areas revealed an elevated prevalence within intensive care units (ICUs), excluding long-term care due to very limited data. Additionally, the presence of invasive medical devices was linked to a higher HAI prevalence, reaching 31.3% among intubated patients.

Among the 1,385 HAIs reported, the most common types were bloodstream infections (BSI, 262 cases, 18.9%), followed by urinary tract infections (UTI, 237 cases, 17.1%), SARS-CoV-2 infections (236 cases, 17.0%), pneumonia and lower respiratory tract infections (PN-LRTI, 231 cases, 16.7%), surgical site infections (SSI, 152 cases, 11.0%), and gastrointestinal tract infections (GI, 103 cases, 7.4%). See Table  2 for complete results.

Isolated microorganisms

Laboratory detection was achieved for 887 HAIs (64% of total HAIs), with 1,039 microorganisms isolated (up to 2 microorganisms per HAI). A total of 71 different pathogens were identified. The most frequently isolated microorganisms included SARS-CoV-2 (145, 14%), E. coli (128, 12.3%), K. pneumoniae (108, 10.4%), S. aureus (94, 9%), and P. aeruginosa (77, 7.4%). Figure  1 shows the most frequently isolated microorganisms per HAI type.

Regarding antibiotic resistance, S. aureus was resistant to oxacillin in 35.3% ( n  = 30) of cases; K. pneumoniae was resistant to third generation cephalosporins in 53.4% ( n  = 55) of cases and in 21.8% ( n  = 22) of cases to carbapenems; P. aeruginosa and A. baumannii were respectively resistant to carbapenems in 24.2% ( n  = 16) and 89% ( n  = 8) of cases.

There were also 4 confirmed cases (0.6%) and 1 possible case (0.1%) of pan-drug-resistant microorganisms, meaning they were resistant to all tested antibiotics. These cases included two A. baumannii , one K. pneumoniae , and one P. aeruginosa , with one possible case of K. pneumoniae .

Distribution of HAIs per site and most frequently isolated microorganisms

• Antimicrobial use

5,225 patients (42.1%, 95% CI 41.3–43.0%) were on systemic antimicrobial therapy. The total number of antimicrobial therapies was 6,884, with each patient receiving 1.32 medications on average. In Table  3 , we provide data regarding antimicrobial use stratified by area of care (top 5), most frequently used molecule (top 5), antimicrobial class (top 5), AWaRe classification and clinical indication. The prevalence of patients on therapy varied significantly across the areas, with the highest prevalence registered in ICUs. The most used antimicrobials were Piperacillin associated with enzyme inhibitors and Ceftriaxone. According to the WHO AWaRe classification [ 21 , 22 ], the most used antibiotics belonged to the Watch class, followed by the Access class and the Reserve class; for 300 antimicrobials the AWaRe classification was not applicable (antifungal and antituberculosis drugs). Antimicrobials were mostly used to treat community-acquired infections followed by HAIs.

Upon further analysis, antibiotics used for treating infections (including community-acquired, healthcare-associated, and long-term-care-associated) mostly belonged to the Watch class (2,906, 71.8%), followed by the Access class (792, 19.6%) and the Reserve class (349, 8.6%).

Antimicrobials used to treat infections were mostly used to treat pneumonia (1,301, 31.4%), followed by bacteremia with laboratory confirmation (370, 8.9%), lower-urinary-tract infections (362, 8.7%) and intra-abdominal sepsis (332, 8.0%).

Antibiotics used for prophylaxis mostly belonged to the Access class (1,267, 67.4%), followed by the Watch class (595, 31.6%) and the Reserve class (18, 1.0%).

Antimicrobials used for surgical prophylaxis lasting more than one day (43% of all surgical prophylaxis), compared to prophylaxis lasting one day or less belonged significantly more to the Watch class (32.3% vs. 14.9%, p  < 0.001) and significantly less to the Access class (66.1% vs. 84.8%, p  < 0.001). Figure  2 summarize the distribution of the AWaRe classification for each antibiotic indication.

Distribution of the AWaRe classification for each antibiotic indication

Legend: CI: community-acquired infections; LI: long-term acquired infections; HI: Healthcare-associated infections; SP1: Surgical prophylaxis (single dose); SP2: Surgical prophylaxis (1 day); SP3: Surgical prophylaxis (> 1 day); MP: Medical prophylaxis

In 2022 Lombardy, the most populated Italian region, participated in the PPS-3 collecting data from over 12,000 patients. The overall prevalence of HAIs, excluding healthcare-associated COVID-19, registered in Lombardy (8,4%) reveals an upward trend when compared to the preceding national survey (8.0%) [ 23 ] and surpasses the latest available European average (6.5%) [ 10 ].

Among the four most frequent infections (75% of all HAIs), it is noteworthy the significant rise in BSI, twice the European rate in 2016-17 and surpassing Italian figures from 2016 [ 23 , 24 ].

Furthermore, the study has shown a higher prevalence of HAIs in the ≥ 65 age group with a total prevalence increasing up to 12.5%; in groups with a more severe McCabe score due to the frailty of the patients; in intensive care units (22%), with twice the value of the medical wards (11.4%), rehabilitation units (11.2%) and surgical departments (10.3%); in the presence of invasive MDs with a prevalence of 31.1% in intubated patients, 24.8% in patients with central venous catheter and 17.2% in patients with a urinary catheter.

Of the 12,412 patients, 42.1% were on antimicrobial therapy, consistent with the previously recorded Italian data (44.5%) [ 23 ], but higher than the European data (30.5%) [ 25 ].

In terms of antibiotic class selection, according to the World Health Organization’s AWaRe (Access-Watch-Reserve) classification [ 21 , 22 ], results are not reassuring: the Access group (antibiotics less likely to induce resistance), the Watch group (broader spectrum and need for restricted use) and the Reserve group (last resort indication) account respectively for 32.7%, 57% and 5.9% of total antibiotic consumption. While the general WHO target for Access antibiotics is set at 60%, it is worth noting that there are no specific targets outlined for hospitals: our data align with ECDC’s data which shows for 2022 an average usage of 5.2% for the Reserve group (the sole category represented for hospital use) in Europe and 7.8% in Italy [ 26 ].

Regarding the indication, medical prophylaxis (11.9%) showed a significant reduction compared to the 23.3% observed in the Italian PPS-2 [ 23 ]; however, it still represents a high percentage, at least in part due to misclassification in the collection of data owing to the circumstance that data collectors could have reported erroneously empirical therapies as medical prophylaxes. The use for surgical prophylaxis is significant (17.1%) and particularly critical when its duration exceeds one day (43% of surgical prophylaxis), an unjustified use, and more likely to be carried out using an antibiotic from the Watch class, resulting in a dual error.

From a microbiological perspective, investigations were conducted only on 64% of the total HAIs, however they showed a significant pattern of antibiotic resistance. Focusing on antibiotic resistance data of the Italian PPS-2 [ 23 ] and the results of our study, we observed a lower level of antimicrobial resistance of S. aureus to oxacillin (47.4% and 35.3%), of K. pneumoniae to third-generation cephalosporins (68.1% and 53.4%) and to carbapenems (49.5% and 21.8%), and of P. aeruginosa to carbapenems (31% and 24.2%). Notably, antimicrobial resistance of A. baumannii to carbapenems was higher (76.9% and 89%). It should be noted, however, that the data were collected based on information provided by ACHs, and there may have been instances where pathogen isolation was assigned without a comprehensive evaluation of the precise infection aetiology.

Strengths and limitations of the study

There are some limitations in this study, predominantly stemming from the extensive nature of large multicenter surveys. We have to take into account that, despite healthcare professionals collecting data using a standardized definition of HAI, errors and misinterpretations among data collectors may have occurred. To address this issue, an initial coherence analysis was conducted and is provided in Appendix 1 . However, these measures might not be sufficient, particularly given the absence of an external validation process, as recommended by the ECDC protocol [ 17 ].

Furthermore, it is important to note that the prevalence of infections differs from the incidence, as data collected on a single day may not be representative of reality, especially for hospitals with fewer patients. As demonstrated by Gastmeier et al., prevalence studies tend to show a higher rate of infection compared to incidence rate studies [ 27 ], but incidence studies are costly, time-consuming and require many resources, making it difficult to involve a large number of hospitals as effectively as in prevalence studies, complicating the gathering and comprehensive comparison of results.

Additionally, the healthcare system in Lombardy is constituted of highly specialized hospitals that attract patients from across the country, leading to higher complexity of cases which, in turn, increases the likelihood of admitting patients with greater frailty compared to other regional contexts, potentially resulting in a more significant impact of HAIs.

Despite these limitations, the data analyzed in this study offer a valuable contribution to understanding the impact of HAIs on patients admitted to ACHs in Lombardy and can be used as a baseline indicator for future comparisons.

Implications for policy and practice

The first two editions of the Point Prevalence Survey (PPS), conducted in 2011 and 2016, paved the way for establishing a surveillance system for healthcare-associated infections (HAI) and antibiotic use at both the European and national levels. This was achieved through the structuring of extensive databases capable of supporting targeted analyses that can facilitate interventions aimed at improving the quality of care provided. The Lombardy Region participated in both previous editions, despite the participation of few hospitals. However, in the third prevalence study, there was a significant increase in participation which has been crucial in developing the first regional report. The analysis of the collected data will help identify common challenges, provide new tools to promote and strengthen the understanding of phenomena, enhance the skills of all stakeholders, and offer recommendations and strategies for managing HAIs and the conscious use of antibiotics. Furthermore, it represents an important regional benchmarking for internal analysis that every ACH is called upon to conduct to establish concrete improvement objectives.

Healthcare-Associated Infections pose a significant concern in the current healthcare setting, with substantial implications for both patients and ACHs. PPS-3 in Lombardy facilitated the collection of data on HAIs from ACHs, providing a structured benchmarking framework to guide regional health policies and reduce the burden of HAIs. Along with prevention activities and prudent use of antimicrobials, surveillance protocols of HAIs, like the ECDC Point Prevalence Survey, must be adopted at all healthcare institutional levels (hospital, regional, national, international) as they are an indispensable source of data for the implementation of routinary and extraordinary initiatives for the prevention and control of HAIs in the antimicrobial resistance era.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available. All Lombardy hospital data are accessible solely to the regional coordinator in accordance with the privacy rules outlined by the PPS protocol. However, they can be made available from the corresponding author upon reasonable request, upon authorization from the regional coordinator.

Abbreviations

Healthcare-Associated Infection

European Centre for Disease Prevention and Control

Point Prevalence Survey-3

Severe Acute Respiratory Syndrome coronavirus-2

Intensive Care Unit

World Health Organization

Access, Watch and Reserve

EEA, European Union/ European Economic Area

United Kingdom

Acute Care Hospital

General Data Protection Regulation

Surgical Site Infections

Confidence Interval

Interquartile Range

Medical Device

Bloodstream Infections

Urinary Tract Infections

Pneumonia and Lower Respiratory Tract Infections

Gastrointestinal tract Infections

Cassini A, et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. Lancet Infect Dis. 2019;19(1). https://doi.org/10.1016/S1473-3099(18)30605-4

Stewart S, et al. Impact of healthcare-associated infection on length of stay. J Hosp Infect. 2021;114. https://doi.org/10.1016/j.jhin.2021.02.026

Manoukian S, et al. Bed-days and costs associated with the inpatient burden of healthcare-associated infection in the UK. J Hosp Infect. 2021;114. https://doi.org/10.1016/j.jhin.2020.12.027

Eyal Z et al. Health care-associated infections: a meta-analysis of costs and financial impact on the US health care system. JAMA Intern Med, 02120, 2013.

Roberts RR, et al. Costs attributable to healthcare-acquired infection in hospitalized adults and a comparison of economic methods. Med Care. 2010;48(11). https://doi.org/10.1097/MLR.0b013e3181ef60a2

Murray CJ, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399(10325). https://doi.org/10.1016/S0140-6736(21)02724-0

United Nations Environment Programme. Bracing for Superbugs: strengthening environmental action in the One Health response to antimicrobial resistance. Geneva, 2023.

Trivedi KK, et al. Implementing strategies to prevent infections in acute-care settings. Infect Control Hosp Epidemiol. 2023;44(8). https://doi.org/10.1017/ice.2023.103

Regional Committee for Europe. Sixty-first Regional Committee for Europe: Baku, 12–15 September 2011: European strategic action plan on antibiotic resistance, 2011.

Suetens C, et al. Prevalence of healthcare-associated infections, estimated incidence and composite antimicrobial resistance index in acute care hospitals and long-term care facilities: results from two European point prevalence surveys, 2016 to 2017. Eurosurveillance. 2018;23(46). https://doi.org/10.2807/1560-7917.ES.2018.23.46.1800516

Conferenza Stato-Regioni, Piano Nazionale di Contrasto all’Antibiotico-Resistenza (PNCAR) 2022–2025, 2022.

Conferenza Stato-Regioni, Piano Nazionale della Prevenzione 2020–2025, 2020.

Lombardia R. Piano Regionale della Prevenzione 2021–2025, 2021.

European Centre for Disease Prevention and Control. Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals, 2013.

Zarb P et al. European Centre for Disease Prevention and Control. Surveillance report - point prevalence survey of healthcare associated infections and antimicrobial use in European acute care hospitals, 2012.

European Centre for Disease Prevention and Control. Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals, 2016–2017, 2023.

European Centre for Disease Prevention and Control. Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals – protocol version 6.1. Stockholm: ECDC; 2022., in protocol version 6.1, Stockholm, 2022. https://doi.org/10.2900/017250

Ministero della Salute. Lettera per Regioni PPS: Indagine sulla prevalenza puntuale (PPS-3) delle infezioni correlate all’assistenza sanitaria (ICA) e sull’utilizzo di antimicrobici negli ospedali italiani per acuti: Informazioni preliminari.

Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42(2). https://doi.org/10.1016/j.jbi.2008.08.010

Harris PA, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95. https://doi.org/10.1016/j.jbi.2019.103208

Zanichelli V, et al. The WHO AWaRe (Access, Watch, Reserve) antibiotic book and prevention of antimicrobial resistance. Bull World Health Organ. 2023;101(4). https://doi.org/10.2471/BLT.22.288614

Organization WH. Access, Watch, Reserve, classification of antibiotics for evaluation and monitoring of use, 2021.

U. di T. Dipartimento Scienze della Salute Pubblica e Pediatriche, Secondo studio di prevalenza italiano sulle infezioni correlate all’assistenza e sull’uso di antibiotici negli ospedali per acuti – Protocollo ECDC, 2018.

Vicentini C, et al. Point prevalence data on antimicrobial usage in Italian acute-care hospitals: evaluation and comparison of results from two national surveys (2011–2016). Infect Control Hosp Epidemiol. 2020;41(5). https://doi.org/10.1017/ice.2020.18

Plachouras D, et al. Antimicrobial use in European acute care hospitals: results from the second point prevalence survey (PPS) of healthcare-associated infections and antimicrobial use, 2016 to 2017. Eurosurveillance. 2018;23(46). https://doi.org/10.2807/1560-7917.ES.23.46.1800393

European Centre for Disease Prevention and Control. Antimicrobial consumption in the EU/EEA, annual epidemiological report for 2022. ECDC, 2023.

Gastmeier P, Bräuer H, Sohr D, Gastmeier P, Bräuer H, Sohr D, et al. Converting incidence and prevalence data of nosocomial infections results from eight hospitals. Infect Control Hosp Epidemiol. 2001;22(1):31–4. https://doi.org/10.1086/501821 .

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Antonio Antonelli, Maria Elena Ales, Greta Chiecca, Zeno Dalla Valle, Emanuele De Ponti, Cristina Renzi & Carlo Signorelli

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All authors conceived and designed the work. dr. Danilo Cereda and dr. Lucia Crottogini contributed to acquisition and critical interpretation of data. dr. Antonio Antonelli and dr. Zeno Dalla Valle contributed substantively to the analysis of data. dr. Antonio Antonelli, dr. Maria Elena Ales, dr. Greta Chiecca, dr. Zeno Dalla Valle, dr. Emanuele De Ponti, and dr. Matteo Moro contributed to writing the manuscript. dr. Matteo Moro, prof. Cristina Renzi, and prof. Carlo Signorelli substantively revised the work. All authors read and approved the final manuscript.

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Supplementary Material 1: Coherence analysis. Coherence analysis identifying records with logical inconsistencies and corrective actions taken for each.

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Antonelli, A., Ales, M.E., Chiecca, G. et al. Healthcare-associated infections and antimicrobial use in acute care hospitals: a point prevalence survey in Lombardy, Italy, in 2022. BMC Infect Dis 24 , 632 (2024). https://doi.org/10.1186/s12879-024-09487-7

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Research and Development 3 in Destiny 2, NES003 Explained

Stuck on the Research and Development 3 quest in Destiny 2? Can't find Specimen ID NES003? Our guide explains why and what to do next!

Destiny 2 seasonal content keeps players engaged with fresh challenges each week. One such challenge, Research and Development 3, has recently gained attention for its elusive objective: finding Specimen ID: NES003 . This particular quest has caused some frustration as the specimen itself hasn't been discovered yet. Keep reading if you've accepted the quest and are wondering why there's no progress.

Destiny 2 – How to Start Research and Development

If you're one of the many Destiny 2 players who accepted the Research and Development 3 quest after finishing the seasonal story mission, but can't find Specimen ID: NES033, don't worry! This quest is actually marked for week 4 of the seasonal challenges in Episode Echoes, and we're currently still in week 3. The developers are aware of this , and it will be reset in the following week.

Here are all week 4 challenges:

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Children's Hospital of Philadelphia Researchers Find Elementary Age Children Experience More Concussions During Activities Unrelated to Sports

Children between 5 and 12 are more likely to experience worse symptoms and delays in specialist care if injured during recreation or other non-sport activities

PHILADELPHIA , June 27, 2024 /PRNewswire/ -- Researchers from Children's Hospital of Philadelphia (CHOP) found that young children between the ages of 5 and 12 were more likely to experience a concussion from recreation and other non-sport activities, yet those injuries were not seen by specialists until days later compared with sports-related concussions in the same age group.

This study suggests concussion research is needed for children outside of sports and that providing more resources and education to those providers diagnosing most concussions in this age group, particularly emergency departments and primary care, could reduce inequities in concussion care regardless of the mechanism of injury by which these patients experience concussions. The findings were recently published by the Journal of Pediatrics .

Adolescents experience high rates of sports- and recreation-related injuries, but the rate of injuries among children ages 5 through 12 is still high, at about 72.7 injuries per 1000 children. More than half of children in this age range participate in sports, as daily physical activity is recommended for optimal health and development, but with these activities comes a risk of pediatric concussion.

Most of the research into pediatric concussions has focused on adolescents and sports, which highlights a need to study concussions in younger children across all mechanisms of injury. Prior studies from nearly a decade ago reported the importance of non-sports and recreation-related concussions in elementary age children. Injuries sustained in these settings are marked by key differences in supervision at the time of injury that can influence how quickly a concussion is recognized, affecting access to and timing of care, which can lead to longer recovery times.

"In prior research, recreation-related injuries are not often differentiated from sports-related injuries, yet this study shows that these injuries can be just as serious and occur more frequently in this age group, suggesting that education and awareness about concussion needs to be emphasized to those who interact with children in these less structured settings," said senior study author Kristy Arbogast , PhD , director of the Center for Injury Research and Prevention and co-director of the Minds Matter Concussion Program at CHOP. "Patients injured outside of sports and recreation experienced a higher burden of symptoms and more changes to daily life, and delays in appropriate care could exacerbate these negative effects."

Using contemporary data from a pediatric concussion registry, researchers examined this age range and characterized concussions by their mechanisms of injury, distinguishing between injuries that occurred in organized sports and those that occurred outside of sports. They separated recreation, such as gym class, free play, or non-competitive sporting activities like biking, from other non-sports mechanisms, like motor vehicle crashes or falls, owing to the role of unstructured play in this age group. A total of 1,141 patients between the ages of 5 and 12 with concussions were evaluated within four weeks of injury and were included in this analysis. The researchers assessed whether the injury occurred during sports, recreation, or some other mechanism of injury ("non-sports-or-recreation-related"). Variations in demographics, point of healthcare entry, and clinical signs were evaluated across mechanisms.

The study found that recreation-related injuries were the most common in this age group at 37.3% of injuries, followed by non-sports-or-recreation-related concussions at 31.9%. These injuries were more likely to be seen first in the emergency department compared to sports-related concussions. Importantly, patients with recreation- or non-sports or recreation-related concussions were first evaluated by concussion specialists an average of 2 to 3 days later than sports-related concussions. Patients with concussions outside of sports and recreation also reported worse symptoms, including more visio-vestibular issues and more changes to sleep and other daily habits compared with the other patient groups.

"We see these findings as an opportunity to equip the clinical teams who may see these patients first with the latest tools for concussion diagnosis and management," said study co- author Daniel Corwin , MD , Director of Research Operations in the Division of Emergency Medicine and Emergency Department Lead of the Minds Matter Concussion Program. "These findings could also serve as a basis for school-based resources, including engagement of school nurses, to help address disparities in care across these injuries, particularly in this less well understood elementary age population of patients who sustain their injuries outside of sports."

This study was supported by the National Institute of Neurologic Disorders and Stroke of the National Institutes of Health under award numbers R01NS097549 and the Pennsylvania Department of Health.

Roby et al, "Characteristics of Pediatric Concussion across Different Mechanisms of Injury in 5–12-Year-Olds." J Pediatr. Online June 18, 2024 . DOI: 10.1016/j.jpeds.2024.114157.

About Children's Hospital of Philadelphia : A non-profit, charitable organization, Children's Hospital of Philadelphia was founded in 1855 as the nation's first pediatric hospital. Through its long-standing commitment to providing exceptional patient care, training new generations of pediatric healthcare professionals, and pioneering major research initiatives, the hospital has fostered many discoveries that have benefited children worldwide. Its pediatric research program is among the largest in the country. The institution has a well-established history of providing advanced pediatric care close to home through its CHOP Care Network , which includes more than 50 primary care practices, specialty care and surgical centers, urgent care centers, and community hospital alliances throughout Pennsylvania and New Jersey , as well as the Middleman Family Pavilion  and its dedicated pediatric emergency department in King of Prussia . In addition, its unique family-centered care and public service programs have brought Children's Hospital of Philadelphia recognition as a leading advocate for children and adolescents. For more information, visit https://www.chop.edu .

Contact: Ben Leach Children's Hospital of Philadelphia (609) 634-7906 [email protected]

SOURCE Children's Hospital of Philadelphia

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Rafel RAT, Android Malware from Espionage to Ransomware Operations

Research by: Antonis Terefos, Bohdan Melnykov

Introduction

Android, Google’s most popular mobile operating system, powers billions of smartphones and tablets globally. Known for its open-source nature and flexibility, Android offers users a wide array of features, customization options, and access to a vast ecosystem of applications through the Google Play Store and other sources.

However, with its widespread adoption and open environment comes the risk of malicious activity. Android malware, a malicious software designed to target Android devices, poses a significant threat to users’ privacy, security, and data integrity. These malicious programs come in various forms, including viruses, Trojans, ransomware, spyware, and adware, and they can infiltrate devices through multiple vectors, such as app downloads, malicious websites, phishing attacks, and even system vulnerabilities.

The evolving landscape of Android malware presents challenges for users, developers, and security experts. As attackers employ increasingly sophisticated techniques to evade detection and compromise devices, understanding the nature of Android malware, its distribution methods and effective prevention and mitigation strategies become paramount.

Rafel RAT is an open-source malware tool that operates stealthily on Android devices. It provides malicious actors with a powerful toolkit for remote administration and control, enabling a range of malicious activities from data theft to device manipulation.

Check Point Research has identified multiple threat actors utilizing Rafel, an open-source remote administration tool (RAT). The discovery of an espionage group leveraging Rafel in their operations was of particular significance, as it indicates the tool’s efficacy across various threat actor profiles and operational objectives.

In an earlier publication , we identified  APT-C-35 / DoNot Team  utilizing Rafel RAT. Rafel’s features and capabilities, such as remote access, surveillance, data exfiltration, and persistence mechanisms, make it a potent tool for conducting covert operations and infiltrating high-value targets.

Campaigns Overview & Victims Analysis

We observed around 120 different malicious campaigns, some of which successfully targeted high-profile organizations, including the military sector. While most of the targeted victims were from the United States, China, and Indonesia, the geography of the attacks is pretty vast.

Such campaigns can be considered high-risk, as the fact that the victim’s phone book being exfiltrated could leak sensitive information about other contacts and allow lateral movement within the organization based on that data. Another point of concern is stolen two-factor authentication messages, which could lead to multiple accounts taking over.

The majority of victims had Samsung phones, with Xiaomi, Vivo, and Huawei users comprising the second-largest group among the targeted victims. This result corresponds to the popularity of the devices in various markets.

While certain brands had higher numbers of infected devices, a wide range of models were involved. Therefore, we categorized the models based on their series. Our findings also highlighted that most victims had Google devices (Pixel, Nexus), Samsung Galaxy A & S Series, and Xiaomi Redmi Series.

It’s intriguing to note the distribution of Android versions among the most affected victims. Android 11 is the most prevalent, followed by versions 8 and 5. Despite the variety of Android versions, malware can generally operate across all. However, newer versions of the operating system typically present more challenges for malware to execute its functions or require more actions from the victim to be effective.

One thing we constantly observe in Windows bots is the consistently high number of Windows XP infections, despite the fact that this version reached its End of Life in 2014. We observed the same scenario in infected Android devices. More than 87% of the affected victims are running Android versions that are no longer supported and, consequently, not receiving security fixes.

Technical Analysis

This malware was developed to participate in phishing campaigns. It leverages deceptive tactics to manipulate user trust and exploit their interactions. Upon initiation, the malware seeks the necessary permissions and may also request to be added to the allowlist. Especially when the device’s manufacturer offers extra services for app optimization, this helps to ensure its persistence in the system.

Our investigation uncovered numerous phishing operations utilizing this specific malware variant. Under the guise of legitimate entities, the malware impersonates multiple widely recognized applications, including Instagram, WhatsApp, various e-commerce platforms, antivirus programs, and support apps for numerous services.

Depending on the attacker’s modifications, the malware may request permissions for Notifications or Device Admin rights or stealthily seek minimal sensitive permissions (such as SMS, Call Logs, and Contacts) in its quest to remain undetected. Regardless, the malware commences its operations in the background immediately upon activation. It deploys a Background service that generates a notification with a deceptive label while operating covertly. At the same time, it initiates an InternalService to manage communications with the command-and-control (C&C) server.

The InternalService initiates communication with the (C&C) server, activates location tracking, and begins setting up Text-To-Speech components.

Communication occurs over HTTP(S) protocols, beginning with the initial phase of client-server interaction. This involves transmitting information about the device, including its identifiers, characteristics, locale, country, model specifics, and operator details. Next, a request is sent to the C&C server for the commands to execute on the device.

The range of supported commands and their names may vary depending on the specific malware variant. The table below outlines the fundamental commands found in the original malware sources:

In addition to the primary communication channel, the malware was initially able to send quick messages through the Discord API. During the onboarding process, it notifies the attacker of a new victim’s appearance. This enables attackers to respond swiftly and extract the necessary data from the compromised device.

This communication channel is also used to intercept device notifications. The malware scans the content of these notifications and forwards it to the attackers. This enables the attackers to siphon sensitive data from other applications, such as capturing 2FA codes sent through messaging platforms.

During our analysis, we encountered several protective mechanisms employed by the attackers. These ranged from string encryption and packer usage to various anti-evasion techniques designed to disrupt automated analysis pipelines or render some tools ineffective.

Some of the evasions used can be mitigated by newer versions of the analysis tools.

For more information about evasion techniques, refer to our  Check Point Research Evasion Encyclopedia .

Command & Control

Threat actors who use Rafel are provided with a PHP panel, which operates without the need for a traditional database setup and relies instead on JSON files for storage and management. During installation, the threat actor uses a designated username and password to access the administration panel. Through this interface, the threat actors can monitor and control the infected mobile devices.

Upon logging into the command and control interface, threat actors can access essential information about the infected devices, such as:

• Device  – Phone model
• Version  – Android Version
• Country  – Provides geographical context, allowing threat actors to tailor their malicious activities or campaigns to specific regions or demographics.
• SIM operator  – The mobile network operator associated with the device’s SIM card, which can help track the device’s location.
• Charge  – The current power level of the infected device.
• Is Rooted  – Indicates whether the device is rooted, providing information on the permitted access level.

As the threat actors view bot details within the panel, additional information regarding the device’s specifications and available commands becomes accessible. The panel shows the following extracted device information:

• Language  – Specifies the language setting configured on the infected device.
• RAM  – Provides details about the device’s random access memory (RAM) capacity. This information could indicate whether the device is a sandbox.

In addition, the panel grants the operator access to a suite of phone features and commands that can be executed remotely on the infected device.

The  GetContact  command enables the threat actors to retrieve contact details from the victim’s device, including names and phone numbers. This allows the attackers access to sensitive personal information stored on the device, facilitating identity theft, social engineering attacks, or further exploiting the victim’s contacts for malicious purposes.

Threat actors can retrieve SMS messages containing sensitive information by using the GetSMS command. We observed malicious actors abusing this functionality to obtain two-factor authentication (2FA) details. This presents a significant security risk, as 2FA codes are commonly used to secure accounts and transactions.

The  Application  command provides further information regarding the installed applications on the victim’s devices.

Newer versions of the command and control panel provide extended functionalities, as seen below.

Our analysis of executed bot commands provided valuable insights into the tactics, techniques, and procedures (TTPs) employed by cyber criminals and yielded actionable intelligence.

Deeper Analysis of Campaigns

Check Point Research took a deeper dive into three specific areas of Android infections:

• Ransomware operations
• Two-factor authentication messages that could have led to a 2FA bypass
• Threat actors who hacked Pakistani government sites

The cases we uncovered underscore severe dangers for individuals and corporations operating in the Android ecosystem.

Ransomware Operation Analysis

In its fundamental iteration, the Rafel application possesses all the essential features required for executing extortion schemes effectively. When malware obtains DeviceAdmin privileges, it can alter the lock-screen password. In addition, leveraging device admin functionality aids in preventing the malware’s uninstallation. If a user attempts to revoke admin privileges from the application, it promptly changes the password and locks the screen, thwarting any attempts to intervene.

In addition to its locker functionality, the malware incorporates a variant that encrypts files using AES encryption, employing a predefined key. Alternatively, it may delete files from the device’s storage.

Check Point Research identified a ransomware operation performed using Rafel RAT. The threat actor, who possibly originates from Iran, initially executed typical information-retrieving commands such as:

• device_info  – Get device info.
• application_list  – Get the device application list.
• arama_gecmisi  – Get call logs.
• rehber_oku  – Get contact details.
• sms_oku  – Get SMS messages.

At this point, the operator determines with the information obtained that the victim has any value in terms of espionage and then begins the ransomware operation with these commands:

• deletecalls  – Wipes call history.
• ransomware  – Displays the message “Loda Pakistan” (the victim was from Pakistan).
• changewallpaper  – Change the wallpaper, and message “loda Pakistan.”
• LockTheScreen  – Locks the screen with the message “Loda Pakistan.”
• send_sms  – Sends a message containing the ransom note.
• vibrate  – Vibrate to alert the victim.

The “ransom note” in the form of an SMS message is written in Arabic and provides a Telegram channel to continue the dialogue.

Two-Factor Authentication (2FA)

Our investigations revealed numerous cases where 2FA messages were stolen, potentially leading to a 2FA bypass. Compromised 2FA codes (OTP – one-time passwords) can enable malicious actors to circumvent additional security measures and gain unauthorized access to sensitive accounts and information.

Threat Actors Targeting Government Infrastructure

In one recent case, we identified a threat actor who managed to hack a government website from Pakistan. The actor also installed the Rafel web panel on this server, and we observed infected devices reporting to this C&C.

The hacker  @LoaderCrazy  published his “achievement” on the Telegram channel  @EgyptHackerTeam , with the message in Arabic “ ما نخترقه نترك بصمتنا عليه ” (English:  What we penetrate we leave our mark on )

The Rafel web panel was installed on May 18, 2024, though traces of the hacking date back to April 2023.

The Rafel victims on this C&C are from diverse countries, including the United States, Russia, China, and Romania.

Rafel RAT is a potent example of the evolving landscape of Android malware, characterized by its open-source nature, extensive feature set, and widespread utilization across various illicit activities. The prevalence of Rafel RAT highlights the need for continual vigilance and proactive security measures to safeguard Android devices against malicious exploitation. As cyber criminals continue to leverage techniques and tools such as Rafel RAT to compromise user privacy, steal sensitive data, and perpetrate financial fraud, a multi-layered approach to cybersecurity is essential. Effective mitigation strategies should encompass comprehensive threat intelligence, robust endpoint protection mechanisms, user education initiatives, and stakeholder collaboration within the cybersecurity ecosystem.

Check Point’s  Harmony Mobile  prevents malware from infiltrating mobile devices by detecting and blocking the download of malicious apps in real-time. Harmony Mobile’s unique network security infrastructure—On-device Network Protection—allows you to stay ahead of emerging threats by extending Check Point’s industry-leading network security  technologies  to mobile devices.

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Numbers, Facts and Trends Shaping Your World

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Public Trust in Government: 1958-2024

Public trust in the federal government, which has been low for decades, has increased modestly since 2023 . As of April 2024, 22% of Americans say they trust the government in Washington to do what is right “just about always” (2%) or “most of the time” (21%). Last year, 16% said they trusted the government just about always or most of the time, which was among the lowest measures in nearly seven decades of polling.

When the National Election Study began asking about trust in government in 1958, about three-quarters of Americans trusted the federal government to do the right thing almost always or most of the time.

Trust in government began eroding during the 1960s, amid the escalation of the Vietnam War, and the decline continued in the 1970s with the Watergate scandal and worsening economic struggles.

Confidence in government recovered in the mid-1980s before falling again in the mid-’90s. But as the economy grew in the late 1990s, so too did trust in government. Public trust reached a three-decade high shortly after the 9/11 terrorist attacks but declined quickly after. Since 2007, the shares saying they can trust the government always or most of the time have not been higher than 30%.

Today, 35% of Democrats and Democratic-leaning independents say they trust the federal government just about always or most of the time, compared with 11% of Republicans and Republican leaners.

Democrats report slightly more trust in the federal government today than a year ago. Republicans’ views have been relatively unchanged over this period.

Since the 1970s, trust in government has been consistently higher among members of the party that controls the White House than among the opposition party.

Republicans have often been more reactive than Democrats to changes in political leadership, with Republicans expressing much lower levels of trust during Democratic presidencies. Democrats’ attitudes have tended to be somewhat more consistent, regardless of which party controls the White House.

However, Republican and Democratic shifts in attitudes from the end of Donald Trump’s presidency to the start of Joe Biden’s were roughly the same magnitude.

Among Asian, Hispanic and Black adults, 36%, 30% and 27% respectively say they trust the federal government “most of the time” or “just about always” – higher levels of trust than among White adults (19%).

During the last Democratic administration, Black and Hispanic adults similarly expressed more trust in government than White adults. Throughout most recent Republican administrations, White Americans were substantially more likely than Black Americans to express trust in the federal government to do the right thing.