Search Results (326)

Powdery mildew, caused by Erysiphe quercicola, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R² values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases. Full article

Yellow rust (Puccinia striiformis) is a common wheat disease that significantly reduces yields, particularly in seasons with cooler temperatures and frequent rainfall. Early detection is essential for effective control, especially in key wheat-producing regions such as Southern Dobrudja, Bulgaria. This study presents a UAV-based approach for detecting yellow rust using only RGB imagery and deep learning for pixel-based classification. The methodology involves data acquisition, preprocessing through histogram equalization, model training, and evaluation. Among the tested models, a UnetClassifier with ResNet34 backbone achieved the highest accuracy and reliability, enabling clear differentiation between healthy and infected wheat zones. Field experiments confirmed the approach’s potential for identifying infection patterns suitable for precision fungicide application. The model also showed signs of detecting early-stage infections, although further validation is needed due to limited ground-truth data. The proposed solution offers a low-cost, accessible tool for small and medium-sized farms, reducing pesticide use while improving disease monitoring. Future work will aim to refine detection accuracy in low-infection areas and extend the model’s application to other cereal diseases. Full article

Background: Perioperative care is an integral part of the procedure of a surgical operation, with strictly defined rules. The need to upgrade and improve some individual long-term processes aims at optimal patient care and the provision of high-level health services. Therefore, preoperative care is drawn up with new data resulting from the evolution of technology to upgrade the procedures that need improvement. According to the international literature, a factor considered to be of major importance is high preoperative anxiety and its effects on the patient’s postoperative course. High preoperative anxiety is postoperatively responsible for prolonged hospital stays, increased postoperative pain, decreased effect of anesthetic agents, increased amounts of analgesics, delayed healing of surgical wounds, and increased risk of infections. The use of Virtual Reality technology appears as a new method of managing preoperative anxiety. Objective: This study investigates the effect and effectiveness of Virtual Reality (VR) technology in managing preoperative anxiety in adult patients. Methods: A literature review was performed on 193 articles, published between 2017 and 2024, sourced from the scientific databases PubMed and Cochrane, as well as the trial registry ClinicalTrials, with a screening and exclusion process to meet the criterion of investigating VR technology’s effectiveness in managing preoperative anxiety in adult patients. This systematic review was conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Results: Out of the 193 articles, 29 were selected. All articles examined the efficacy of VR in adult patients (≥18) undergoing various types of surgery. The studies represent a total of 2.354 participants from 15 countries. There are two types of VR applications: distraction therapy and patient education. From the studies, 14 (48%) used the distraction VR intervention, 14 (48%) used the training VR intervention, and 1 (4%) used both VR interventions, using a range of validated anxiety scales such as the STAI, VAS-A, APAIS, and HADS. Among the 29 studies reviewed, 25 (86%) demonstrated statistically significant reductions in preoperative anxiety levels following the implementation of VR interventions. VR technology appears to manage preoperative anxiety effectively. It is a non-invasive and non-pharmacological intervention with minimal side effects. Conclusions: Based on the review, the management of preoperative anxiety with VR technology shows good levels of effectiveness. Further investigation of the efficacy by more studies and randomized controlled trials, with a larger patient population, is recommended to establish and universally apply VR technology in the preoperative care process as an effective method of managing preoperative anxiety. Full article

Background: This study examined risk factors for syndrome novel coronavirus 2 virus (SARS-CoV-2) infection and self-reported adherence to infection prevention and control (IPC) measures among healthcare workers (HCWs) at a hospital in Thailand near the Myanmar border. Methods: From March to July 2022, HCWs aged ≥ 18 with COVID-19 exposure at Mae Sot General Hospital completed a questionnaire on IPC adherence, training, and COVID-19 knowledge. Nasopharyngeal samples were collected bi-weekly for SARS-CoV-2 testing. A mobile application was used for real-time monitoring of daily symptoms and exposure risks. Chi-square, Fisher’s exact tests, and log-binomial regression were performed to investigate association. Results: Out of 289 (96.3%) participants, 27 (9.9%) tested positive for SARS-CoV-2, with cough reported by 85.2% of cases. Nurse assistants (NAs) had a higher risk of infection (adjusted relative risk [aRR] 3.87; 95% CI: 0.96–15.6). Working in inpatient departments (aRR 2.37; 95% CI: 1.09–5.15) and COVID-19 wards (aRR 5.97; 95% CI: 1.32–26.9) was also associated with increased risk. While 81.7% reported consistent hand hygiene, 37% indicated inadequate IPC knowledge. Conclusions: HCWs, especially NAs and those in high-risk departments, should receive enhanced IPC training. Real-time digital monitoring tools can enhance data collection and HCW safety and are likely to be useful tools for supporting surveillance and data collection efforts. Full article

Respiratory diseases represent a persistent global health challenge, underscoring the need for intelligent, accurate, and personalized diagnostic and therapeutic systems. Existing methods frequently suffer from limitations in diagnostic precision, lack of individualized treatment, and constrained adaptability to complex clinical scenarios. To address these challenges, our study introduces a modular AI-powered framework that integrates an audio-based disease classification model with simulated molecular biomarker profiles to evaluate the feasibility of future multimodal diagnostic extensions, alongside a synthetic-data-driven prescription recommendation engine. The disease classification model analyzes respiratory sound recordings and accurately distinguishes among eight clinical classes: bronchiectasis, pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma, chronic obstructive pulmonary disease (COPD), bronchiolitis, and healthy respiratory state. The proposed model achieved a classification accuracy of 99.99% on a holdout test set, including 94.2% accuracy on pediatric samples. In parallel, the prescription module provides individualized treatment recommendations comprising drug, dosage, and frequency trained on a carefully constructed synthetic dataset designed to emulate real-world prescribing logic.The model achieved over 99% accuracy in medication prediction tasks, outperforming baseline models such as those discussed in research. Minimal misclassification in the confusion matrix and strong clinician agreement on 200 prescriptions (Cohen’s $\kappa$ = 0.91 [0.87–0.94] for drug selection, 0.78 [0.74–0.81] for dosage, 0.96 [0.93–0.98] for frequency) further affirm the system’s reliability. Adjusted clinician disagreement rates were 2.7% (drug), 6.4% (dosage), and 1.5% (frequency). SHAP analysis identified age and smoking as key predictors, enhancing model explainability. Dosage accuracy was 91.3%, and most disagreements occurred in renal-impaired and pediatric cases. However, our study is presented strictly as a proof-of-concept. The use of synthetic data and the absence of access to real patient records constitute key limitations. A trialed clinical deployment was conducted under a controlled environment with a positive rate of satisfaction from experts and users, but the proposed system must undergo extensive validation with de-identified electronic medical records (EMRs) and regulatory scrutiny before it can be considered for practical application. Nonetheless, the findings offer a promising foundation for the future development of clinically viable AI-assisted respiratory care tools. Full article

Pseudomonas aeruginosa is a highly adaptable opportunistic pathogen with significant clinical relevance in both human and veterinary medicine. Despite its well-documented role in hospital-acquired infections in human healthcare settings, its persistence and transmission within veterinary clinics remain underexplored. This review highlights the overlooked status of veterinary facilities as environmental reservoirs and amplification points for multidrug-resistant (MDR) P. aeruginosa, emphasizing their relevance to One Health surveillance. We examine the bacterium’s environmental survival strategies, including biofilm formation, resistance to disinfectants, and tolerance to nutrient-poor conditions that facilitate the long-term colonization of moist surfaces, drains, medical equipment, and plumbing systems. Common transmission vectors are identified, including asymptomatic animal carriers, contaminated instruments, and the hands of veterinary staff. The review synthesizes current data on antimicrobial resistance in environmental isolates, revealing frequent expression of efflux pumps and mobile resistance genes, and documents the potential for zoonotic transmission to staff and pet owners. Key gaps in environmental monitoring, infection control protocols, and genomic surveillance are identified, with a call for standardized approaches tailored to the veterinary context. Control strategies, including mechanical biofilm disruption, disinfectant cycling, effluent monitoring, and staff hygiene training, are evaluated for feasibility and impact. The article concludes with a One Health framework outlining cross-species and environmental transmission pathways. It advocates for harmonized surveillance, infrastructure improvements, and intersectoral collaboration to reduce the risk posed by MDR P. aeruginosa within veterinary clinical environments and beyond. By addressing these blind spots, veterinary facilities can become proactive partners in antimicrobial stewardship and global resistance mitigation. Full article

Background: Clostridioides difficile infection (CDI) is a major concern in hospital-acquired infections. C. difficile spores can survive on surfaces for months and require sporicidal disinfection for elimination. The use of disinfectants should be based on laboratory-confirmed sporicidal activity, tested according to current standards in suspension and carrier tests. Further evaluation of disinfectant efficacy should occur in clinical settings by analyzing reductions in CDI incidence. This study aims to conduct a retrospective analysis of the impact of a new disinfection protocol and concurrent changes in antibiotic consumption on the incidence of healthcare-acquired CDI (HA-CDI). Methods: This retrospective, single-center study assessed the impact of a chlorine dioxide-based disinfection protocol on HA-CDI across three periods: pre-intervention, intervention, and post-intervention. An interrupted time series analysis (ITS) with a Poisson distribution was used to evaluate the incidence of HA-CDI, while antibiotic consumption data were analyzed to identify any correlation with CDI infection rates. Results: Incidence Rate Ratio (IRR) before the intervention is 1.00, serving as the reference value. During the intervention period, the IRR is 0.79 (95% CI: 0.42–1.36; p = 0.43), indicating a decrease in the incidence of infections compared to the pre-intervention period, although this result is not statistically significant. After the intervention, the IRR is 0.53 (95% CI: 0.26–0.97; p = 0.057), suggesting a further reduction in the incidence of CDI; this result is on the borderline of statistical significance (p = 0.057), indicating a potential effect of the intervention, albeit without full statistical certainty. Conclusions: The absence of a CDI surge despite increased antibiotic consumption highlights the synergistic relationship between antibiotic stewardship and rigorous infection control practices. The combination of the improved disinfection protocol and comprehensive staff training proved remarkably effective in mitigating CDI risk. Cleaning and disinfection in healthcare facilities is crucial for the prevention of healthcare-associated infections. Full article

Background: Diabetic foot ulcers (DFUs) are a severe complication of diabetes and are characterized by impaired wound healing and a high amputation risk. Exosomes—which are nanovesicles carrying proteins, RNAs, and lipids—mediate intercellular communication in wound microenvironments, yet their biomarker potential in DFUs remains underexplored. Methods: We analyzed transcriptomic data from GSE134431 (13 DFU vs. 8 controls) as a training set and validated findings in GSE80178 (6 DFU vs. 3 controls). A sum of 7901 differentially expressed genes (DEGs) of DFUs were detected and intersected with 125 literature-curated exosome-related genes (ERGs) to yield 51 candidates. This was followed by GO/KEGG analyses and a PPI network construction. Support vector machine–recursive feature elimination (SVM-RFE) and the Boruta random forest algorithm distilled five biomarkers (DIS3L, EXOSC7, SDC1, STX11, SYT17). Expression trends were confirmed in both datasets. Analyses included nomogram construction, functional and correlation analyses, immune infiltration, GSEA, gene co-expression and regulatory network construction, drug prediction, molecular docking, and RT-qPCR validation in clinical samples. Results: A nomogram combining these markers achieved an acceptable calibration (Hosmer–Lemeshow p = 0.0718, MAE = 0.044). Immune cell infiltration (CIBERSORT) revealed associations between biomarker levels and NK cell and neutrophil subsets. Gene set enrichment analysis (GSEA) implicated IL-17 signaling, proteasome function, and microbial infection pathways. A GeneMANIA network highlighted RNA processing and vesicle trafficking. Transcription factor and miRNA predictions uncovered regulatory circuits, and DGIdb-driven drug repurposing followed by molecular docking identified Indatuximab ravtansine and heparin as high-affinity SDC1 binders. Finally, RT-qPCR validation in clinical DFU tissues (n = 5) recapitulated the bioinformatic expression patterns. Conclusions: We present five exosome-associated genes as novel DFU biomarkers with diagnostic potential and mechanistic links to immune modulation and vesicular transport. These findings lay the groundwork for exosome-based diagnostics and therapeutic targeting in DFU management. Full article

Background: Total laparoscopic hysterectomy (TLH) is widely accepted as the preferred minimally invasive technique for the treatment of benign gynecologic conditions. However, significant heterogeneity persists in the literature regarding the operative sequence, particularly for steps such as uterine artery ligation, ureteral identification, and vaginal cuff closure. This lack of standardization may affect complication rates, reproducibility in surgical training, and procedural efficiency. The objective of this study was to develop and evaluate a standardized, anatomically justified surgical protocol for TLH primarily designed for training purposes but applicable to most clinical cases. Methods: This retrospective observational study analyzed 109 patients who underwent TLH between January 2016 and July 2020 at a single tertiary care center. A fixed sequence of surgical steps was applied in all cases, emphasizing early uterine artery ligation at its origin, broad ligament fenestration above the ureter, and laparoscopic figure-of-eight vaginal cuff closure. Patient demographics, operative data, and perioperative outcomes were extracted and analyzed. Results: The mean operative time was 67.2 ± 18.4 min, and the mean uterine weight was 211.9 ± 95.3 g. Intraoperative complications were observed in 3.7% of cases and included bladder injury in 1.8% and small bowel injury in 1.8%, all of which were managed laparoscopically without conversion. Vaginal cuff dehiscence occurred in 1.8%, and postoperative vaginal bleeding in 3.7% of patients. One patient (0.9%) required reoperation due to a vaginal cuff hematoma/abscess. No postoperative infections requiring intervention were reported. The mean hemoglobin drop on the first postoperative day was 1.2 ± 0.9 g/dL. Conclusions: Our findings support the feasibility, reproducibility, and safety of a structured TLH protocol based on anatomical landmarks and early vascular control. Widespread adoption of similar protocols may improve consistency and training, with broad applicability in routine surgical practice and potential adaptation in severely complex cases; however, further validation in multicenter studies is warranted. Full article

Increasing the use of healthcare facilities has resulted in the growing production of biomedical waste, which poses health risks to users, health professionals, and the environment. The aim of this research is to study the correlation between governance in Togo’s public health facilities and the quality of biomedical waste management within these facilities. Methods: This was a cross-sectional, descriptive, and analytical study conducted from September to December 2024. It involved 264 public health facilities of all types in all health regions of Togo. Health facilities were selected using the simple random selection technique. Healthcare providers were selected using the reasoned choice technique. The statistical tests used were the chi-square test and logistic regression, which enabled proportions to be compared and confounding factors to be eliminated, respectively. Results: Multivariate analysis revealed a statistically significant association between the organization and training component of governance and the quality of biomedical waste management (BMWM) in health facilities (OR = 3.79; 95% CI [1.79–8.03]; p < 0.001). This relationship suggests that health facilities with functional infection prevention and control (ICP) or BMWM committees, trained staff at all levels (nursing, technical, and administrative), and dedicated waste management personnel are more likely to implement compliant waste management practices. Analyses of the data also revealed that, among the criteria for assessing the quality of biomedical waste management (BMWM), the most significant were sorting (OR = 1.482; 95% CI [1.286; 1.708]), quantification (OR = 2.026; 95% CI [1.491; 2.753]), transportation (OR = 1.403; 95% CI [1.187; 1.66]), and disposal infrastructure (OR = 1.604; 95% CI [1.298; 1.982]). The application of this grid shows that 17.8% of the health facilities surveyed had a score equal to or above 80% on all the criteria used to assess the quality of biomedical waste management, and they were therefore managing waste in an “acceptable” manner. The study highlights key findings in biomedical waste management practices, providing actionable insights for improving public health safety. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

Tuberculosis (TB) is a major global health threat, with the current Bacillus Calmette–Guérin (BCG) vaccine having limited efficacy against adult pulmonary disease. Trained immunity (TI) is a form of innate immune memory that enhances antimicrobial defense. It is characterized by the epigenetic and metabolic reprogramming of innate immune cells and holds promise as a promising approach to prevent TB. In this study, we investigated the capacity of heparin-binding hemagglutinin (HBHA), a methylated antigen of Mycobacterium tuberculosis, to induce TI in murine RAW264.7 macrophages, human-derived THP-1 macrophages, and human peripheral blood mononuclear cells (hPBMCs). HBHA-trained macrophages exhibited the enhanced expression of pro-inflammatory cytokines (IL-1β, IL-6, TNF-α) following secondary lipopolysaccharide stimulation. The epigenetic profiling indicated elevated levels of H3K4me1 and H3K4me3 histone marks at cytokine gene loci. Further, metabolic analysis revealed heightened lactate production and the increased expression of glycolytic enzymes. Functionally, HBHA-trained macrophages exhibited improved control of intracellular mycobacteria, as evidenced by a significant reduction in colony-forming units following BCG infection. These findings elucidate that HBHA induces a functional TI phenotype via coordinated epigenetic and metabolic changes, and suggest HBHA may serve as a valuable tool for studying TI and its relevance to host defense against mycobacterial infections, pending further in vivo and clinical validation. Full article

Background: Sexual assault victims involving penetration are at risk of contracting human immunodeficiency virus (HIV). Post-exposure prophylaxis (PEP) can effectively prevent HIV infection if initiated promptly within 72 h following exposure and adhered to for 28 days. Nonetheless, therapeutic adherence amongst sexual assault victims is low. Victim-centered care, provided by specially trained forensic nurses and midwives, may increase adherence. Methods: We conducted a retrospective case–control study to evaluate the impact of sexual assault center (SAC)—centered care on adherence to PEP compared to care received in the emergency department (ED). Data from January 2011 to February 2022 were reviewed. Multivariable logistic regression analysis was employed to determine the association between centralized specific care for sexual assault victims and completion of the 28-day PEP regimen. The secondary outcome assessed was provision of psychological support within 5 days following the assault. Results: We analyzed 856 patients of whom 403 (47.1%) received care at a specialized center for sexual assault victims. Attendance at the SAC, relative to the ED, was not associated with greater probability of PEP completion both in the unadjusted (52% vs. 50.6%; odds ratio [OR]: 1.06, 95% CI: 0.81 to 1.39; p = 0.666) and adjusted (OR: 0.81, 95%CI 0.58–1.11; p = 0.193) analysis. The care provided at the SAC was associated with improved early (42.7% vs. 21.5%; p < 0.001) and delayed (67.3% vs. 33.7%; p < 0.001) psychological support. Conclusions: SAC-centered care is not associated with an increase in PEP completion rates in sexual assault victims beyond the increase associated with improved access to early and delayed psychological support. Other measures to improve PEP completion rates should be developed. What is already known on this topic—Completion rates for HIV post-exposure prophylaxis (PEP) among victims of sexual assault are low. Specialized sexual assault centers, which provide comprehensive care and are distinct from emergency departments, have been suggested as a potential means of improving treatment adherence and completion rates. However, their actual impact on treatment completion remains unclear. What this study adds—This study found that HIV PEP completion rates in sexual assault victims were not significantly improved by centralized care in a specialized sexual assault center when compared to care initiated in the emergency department and continued within a sexually transmitted infection clinic. However, linkage to urgent psychological and psychiatric care was better in the specialized sexual assault center. How this study might affect research, practice or policy—Healthcare providers in sexual assault centers should be more aware of their critical role in promoting PEP adherence and improving completion rates. Policymakers should ensure that measures aimed at improving HIV PEP outcomes are implemented at all points of patient contact in these centers. Further research is needed to assess the cost-effectiveness of specialized sexual assault centers. Full article

Background: In just twenty years, three dangerous human coronaviruses—SARS-CoV, MERS-CoV, and SARS-CoV-2 have exposed critical gaps in early detection of emerging viral threats. Current diagnostics remain pathogen-focused, often missing the earliest phase of infection. A virus-agnostic, host-based diagnostic capable of detecting responses to viral intrusion is urgently needed. Methods: We hypothesized that the lungs act as biomechanical instruments, with infection altering tissue tension, wave propagation, and flow dynamics in ways detectable through subaudible vibroacoustic signals. In a matched case–control study, we enrolled 19 RT-PCR-confirmed COVID-19 inpatients and 16 matched controls across two Johns Hopkins hospitals. Multimodal data were collected, including passive vibroacoustic auscultation, lung ultrasound, peak expiratory flow, and laboratory markers. Machine learning models were trained to identify host-response biosignatures from anterior chest recordings. Results: 19 COVID-19 inpatients and 16 matched controls (mean BMI 32.4 kg/m², mean age 48.6 years) were successfully enrolled to the study. The top-performing, unoptimized, vibroacoustic-only model achieved an AUC of 0.84 (95% CI: 0.67–0.92). The host-covariate optimized model achieved an AUC of 1.0 (95% CI: 0.94–1.0), with 100% sensitivity (95% CI: 82–100%) and 99.6% specificity (95% CI: 85–100%). Vibroacoustic data from the anterior chest alone reliably distinguished COVID-19 cases from controls. Conclusions: This proof-of-concept study demonstrates that passive, noninvasive vibroacoustic biosignatures can detect host response to viral infection in a hospitalized population and supports further testing of this modality in broader populations. These findings support the development of scalable, host-based diagnostics to enable early, agnostic detection of future pandemic threats (ClinicalTrials.gov number: NCT04556149). Full article

Background and Objectives: Infectious disease outbreaks are a major challenge for public health systems worldwide, especially for healthcare workers (HCWs). Taif city, in Saudi Arabia, has a high population density and is a tourist destination, which puts it at a high risk of infectious disease outbreaks. Despite its geographical importance, no previous study has been conducted that focuses on assessing the preparedness of healthcare workers in Taif city for managing infectious disease outbreaks. Therefore, we aimed to assess the overall level of preparedness among HCWs in healthcare facilities across Taif city and identify the challenges they face when managing infectious disease outbreaks. Materials and Methods: We conducted a cross-sectional study from October to December 2024 among 294 healthcare workers, using a structured questionnaire. We assessed the sociodemographic characteristics, infection prevention and control (IPC) training received by HCWs, the level of preparedness for managing infectious disease outbreaks, and their level of knowledge (low, moderate, or high). The association between sociodemographic characteristics and knowledge from having received IPC training and the level of preparedness was assessed using binary logistic regression. A p-value of ≤ 0.05 was considered as significant. Results: Around 31.7% of the participants were aged 31–40 years, with 59.2% of them being female. Among the HCWs we assessed, 44.6% were nurses and 31.3% of the HCWs were from hospitals with a bed capacity of over 500. Only 16.3% of HCWs felt fully prepared on a personal level and only 20.7% believed their facility was fully prepared for managing an outbreak. A low level of knowledge was reported among 71.8% of the participants. The odds of having received IPC training were significantly higher among HCWs aged 41–50 years (AOR = 15.7; 95% CI = 4.26–58.1), for those working in the inpatient department (AOR = 6.3; 95% CI = 1.46–27.05), and for those with a moderate level of knowledge (AOR = 0.12; 95% CI = 0.03–0.5). The odds of being fully prepared for an infectious disease outbreak were significantly higher for males (AOR = 2.58; 95% CI = 1.18–5.63) and those working in the in-patient department (AOR = 6.87; 95% CI = 1.7–27.8) and significantly lower for those with a low level of knowledge (AOR = 0.19; 95% CI = 0.06–0.61). Conclusion: Even though many HCWs have undergone IPC training, our findings highlight gaps in both knowledge and overall preparedness among healthcare workers in Taif city. Regular refresher courses, improved resource allocation, and implementing scenario-based emergency drills may help in improving the overall knowledge and preparedness of HCWs. Full article