Search Results (1,107)

Micro-Shell Resonator Gyroscopes have obvious SWaP (Size, Weight and Power) advantages and applicable accuracy for the autonomous navigation of Unmanned Ground Vehicles (UGVs), especially under GNSS-denied environments. When the Micro-Shell Resonator Gyroscope Rotary Inertial Navigation System (MSRG–RINS) operates in the whole-angle mode, its bias varies as an even-harmonic function of the pattern angle, which leads to difficulty in estimating and compensating the bias based on the MSRG in the process of attitude measurement. In this paper, an attitude measurement method based on virtual rotation self-calibration and rotary modulation is proposed for the MSRG–RINS to address this problem. The method utilizes the characteristics of the two operating modes of the MSRG, the force-rebalanced mode and whole-angle mode, to perform virtual rotation self-calibration, thereby eliminating the characteristic bias of the MSRG. In addition, the reciprocating rotary modulation method is used to suppress the residual bias of the MSRG. Furthermore, the magnetometer-aided initial alignment of the MSRG–RINS is carried out and the state-transformation extended Kalman filter is adopted to solve the large misalignment-angle problem under magnetometer assistance so as to enhance the rapidity and accuracy of initial attitude acquisition. Results from real-world experiments substantiated that the proposed method can effectively suppress the influence of MSRG’s bias on attitude measurement, thereby achieving high-precision autonomous navigation in GNSS-denied environments. In the 1 h, 3.7 km, long-range in-vehicle autonomous navigation experiments, the MSRG–RINS, integrated with a Laser Doppler Velocimetry (LDV), attained a heading accuracy of 0.35° (RMS), a horizontal positioning error of 4.9 m (RMS), and a distance-traveled accuracy of 0.24% D. Full article

Introduction: Multiple sclerosis (MS) is a chronic neurodegenerative disease that entails high costs, progressive disability, and reduced quality of life (QoL). Telerehabilitation (TR), supported by new technologies, is emerging as an alternative or complement to in-person rehabilitation, potentially lowering socioeconomic impact and improving QoL. Aim: The objective of this study was to evaluate the effect of TR on the QoL of people with MS compared with in-person rehabilitation or no intervention. Materials and methods: A systematic review of randomized clinical trials was conducted (March–May 2025) following PRISMA guidelines. Searches were run in the PubMed-Medline, EMBASE, PEDro, Web of Science, and Dialnet databases. Methodological quality was assessed with the CASP scale, risk of bias with the Risk of Bias 2 tool, and evidence level and grade of recommendation with the Oxford Classification. The protocol was registered in PROSPERO (CRD420251110353). Results: Of the 151 articles initially found, 12 RCTs (598 total patients) met the inclusion criteria. Interventions included (a) four studies employing video-controlled exercise (one involving Pilates to improve fitness, another involving exercise to improve fatigue and general health, and two using exercises focused on the pelvic floor muscles); (b) three studies using a monitoring app to improve manual dexterity, symptom control, and increased physical activity; (c) two studies implementing an augmented reality system to treat cognitive deficits and sexual disorders, respectively; (d) one platform with a virtual reality headset for motor and cognitive training; (e) one study focusing on video-controlled motor imagery, along with the use of a pain management app; (f) a final study addressing cognitive training and pain reduction. Studies used eight different scales to assess QoL, finding similar improvements between groups in eight of the trials and statistically significant improvements in favor of TR in four. The included trials were of good methodological quality, with a moderate-to-low risk of bias and good levels of evidence and grades of recommendation. Conclusions: TR was more effective in improving the QoL of people with MS than no intervention, was as effective as in-person treatment in patients with EDSS ≤ 6, and appeared to be more effective than in-person intervention in patients with EDSS between 5.5 and 7.5 in terms of QoL. It may also eliminate some common barriers to accessing such treatments. Full article

Action bias, the cognitive tendency to favor action over inaction regardless of its necessity, has been extensively studied across domains such as behavioral economics, organizational behavior, and policy development. However, its manifestation in educational contexts remains critically underexplored. In the digital age, with an abundance of both factual and misleading information, the persistence of action bias within education jeopardizes the cultivation of initial critical thinking capable of addressing multifaceted global challenges. The analysis indicates how institutional norms may foster a performative academic identity that conflates speed and compliance with intellectual competence. Through workshops conducted with university students ranging from undergraduate to PhD levels, participants were tasked with solving a practical yet ambiguous problem to highlight potential cognitive differences across educational stages. Despite prior training in critical thinking, participants consistently defaulted to immediate ideation, bypassing fundamental inquiries into the problem’s legitimacy or broader implications. Using a sensemaking approach, this study demonstrates that reflexive actions are not interpreted as merely cognitive shortcuts but behaviors shaped by educational systems prioritizing visible outputs over critical inquiry. The findings reveal how institutional norms foster a performative academic identity, conflating speed and compliance with intellectual competence. This research challenges traditional pedagogical models, advocating for educational reforms that emphasize assessing the process of learning. By situating action bias within the broader framework of active learning, this study offers actionable insights for educators, policy makers and researchers to foster critical innovative thinking, essential in an increasingly digital future. Full article

Background/Objectives: The glymphatic system is a recently characterized glial-dependent waste clearance pathway in the brain, which makes use of perivascular spaces for cerebrospinal fluid exchange. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) offers a non-invasive method for estimating perivascular flow, but its biological specificity and susceptibility to methodological variation, particularly head position during MRI acquisition, remain as threats to the validity of this technique. This study aimed to assess the prevalence of current DTI-ALPS practices, evaluate the impact of head orientation on ALPS index calculation, and propose a novel computational approach to improve measurement validity. Methods: We briefly reviewed DTI-ALPS literature to determine the use of head-orientation correction strategies. We then analyzed diffusion MRI data from 172 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to quantify the influence of head orientation on ALPS indices computed using the conventional Unrotated-ALPS, a vecrec-corrected ALPS, and the new LD-ALPS method proposed within. Results: A majority of studies employed Unrotated-ALPS, which does not correct for head orientation. In our sample, Unrotated-ALPS values were significantly associated with absolute head pitch (r₁₆₉ = −0.513, p < 0.001), indicating systematic bias. This relationship was eliminated using either vecreg or LD-ALPS. Additionally, LD-ALPS showed more sensitivity to cognitive status as measured by Mini-Mental State Examination scores. Conclusions: Correcting for head orientation is essential in DTI-ALPS studies. The LD-ALPS method, while computationally more demanding, improves the reliability and sensitivity of perivascular fluid estimates, supporting its use in future research on aging and neurodegeneration. Full article

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning. Full article

Data preprocessing and feature engineering play key roles in data mining initiatives, as they have a significant impact on the accuracy, reproducibility, and interpretability of analytical results. This review presents an analysis of state-of-the-art techniques and tools that can be used in data input preparation and data manipulation to be processed by mining tasks in diverse application scenarios. Additionally, basic preprocessing techniques are discussed, including data cleaning, normalisation, and encoding, as well as more sophisticated approaches regarding feature construction, selection, and dimensionality reduction. This work considers manual and automated methods, highlighting their integration in reproducible, large-scale pipelines by leveraging modern libraries. We also discuss assessment methods of preprocessing effects on precision, stability, and bias–variance trade-offs for models, as well as pipeline integrity monitoring, when operating environments vary. We focus on emerging issues regarding scalability, fairness, and interpretability, as well as future directions involving adaptive preprocessing and automation guided by ethically sound design philosophies. This work aims to benefit both professionals and researchers by shedding light on best practices, while acknowledging existing research questions and innovation opportunities. Full article

Background/Objectives: Patient education materials (PEMs) in ophthalmology often exceed recommended readability levels, limiting accessibility for many patients. While organizations like the AAO provide relatively easy-to-read resources, topics remain limited, and other associations’ PEMs are too complex. AI chatbots could help clinicians create more comprehensive, accessible PEMs to improve patient understanding. This study aims to compare the readability of patient education materials (PEMs) written by the American Academy of Ophthalmology (AAO) with those generated by large language models (LLMs), including ChatGPT-4o, Microsoft Copilot, and Meta-Llama-3.1-70B-Instruct. Methods: LLMs were prompted to generate PEMs for 15 common diagnoses relating to cornea and anterior chamber, which was followed by a follow-up readability-optimized (FRO) prompt to reword the content at a 6th-grade reading level. The readability of these materials was evaluated using nine different readability analysis python libraries and compared to existing PEMs found on the AAO website. Results: For all 15 topics, ChatGPT, Copilot, and Llama successfully generated PEMs, though all exceeded the recommended 6th-grade reading level. While initially prompted ChatGPT, Copilot, and Llama outputs were 10.8, 12.2, and 13.2, respectively, FRO prompting significantly improved readability to 8.3 for ChatGPT, 11.2 for Copilot, and 9.3 for Llama (p < 0.001). While readability improved, AI-generated PEMs were on average, not statistically easier to read than AAO PEMs, which averaged an 8.0 Flesch–Kincaid Grade Level. Conclusions: Properly prompted AI chatbots can generate PEMs with improved readability, nearing the level of AAO materials. However, most outputs remain above the recommended 6th-grade reading level. A subjective analysis of a representative subtopic showed that compared to AAO, there was less nuance, especially in areas of clinical uncertainty. By creating a blueprint that can be utilized in human–AI hybrid workflows, AI chatbots show promise as tools for ophthalmologists to increase the availability of accessible PEMs in ophthalmology. Future work should include a detailed qualitative review by ophthalmologists using a validated tool (like DISCERN or PEMAT) to score accuracy, bias, and completeness alongside readability. Full article

Background/Objectives: Diabetic neuropathy (DN) is a prevalent complication of diabetes mellitus, affecting up to 50% of long-term patients and causing significant pain, reduced quality of life, and healthcare burden. Conventional treatments, including anticonvulsants, antidepressants, and opioids, offer limited efficacy and are associated with adverse effects. Emerging evidence suggests that cannabis, acting via the endocannabinoid system, may provide analgesic and neuroprotective benefits. This study evaluates the long-term effects of inhaled cannabis as adjunctive therapy for refractory painful DN. Inhaled cannabis exhibits rapid onset pharmacokinetics (within minutes, lasting 2–4 h) due to pulmonary absorption, targeting CB1 and CB2 receptors to modulate pain and inflammation. Methods: In this prospective, observational study, 52 patients with confirmed painful DN, unresponsive to at least three prior analgesics plus non-pharmacological interventions, were recruited from a single clinic. Following a 1-month washout, patients initiated inhaled medical-grade cannabis (20% THC, <1% CBD), titrated individually. Assessments occurred at baseline and annually for 5 years, including the Brief Pain Inventory (BPI) for pain severity and interference; the degree of pain relief; Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) score; HbA1c; and medication usage. Statistical analyses used repeated-measures ANOVA, Kruskal–Wallis tests, Welch’s t-tests, and Pearson’s correlations via Analyze-it for Excel. Results: Of 52 patients (mean age 45.3 ± 17.8 years; 71.2% male; diabetes duration 23.3 ± 17.8 years), 50 completed follow-up visits. Significant reductions occurred in BPI pain severity (9.0 ± 0.8 to 2.0 ± 0.7, p < 0.001), interference (7.5 ± 1.7 to 2.2 ± 0.9, p < 0.001), LANSS score (19.4 ± 3.8 to 10.2 ± 6.4, p < 0.001), and HbA1c (9.77% ± 1.50 to 7.79% ± 1.51, p < 0.001). Analgesic use decreased markedly (e.g., morphine equivalents: 66.8 ± 49.2 mg to 4.5 ± 9.6 mg). Cannabis dose correlated positively with pain relief (r = 0.74, p < 0.001) and negatively with narcotic use (r = −0.43, p < 0.001) and pain interference (r = −0.43, p < 0.001). No serious adverse events were reported; mild side effects (e.g., dry mouth or euphoria) occurred in 15.4% of patients. Conclusions: Inhaled cannabis showed sustained pain relief, improved glycemic control, and opioid-sparing effects in refractory DN over 5 years, with a favorable safety profile. These findings are associative due to the observational design, and randomized controlled trials (RCTs) are needed to confirm efficacy and determine optimal usage, addressing limitations such as single-center bias and small sample size (n = 52). Future studies incorporating biomarker analysis (e.g., endocannabinoid levels) could elucidate mechanisms and enhance precision in cannabis therapy. Full article

Background: Substance use disorders (SUDs) are highly prevalent, affecting over 48.5 million Americans. Available treatments for SUD remain insufficient, and many patients do not respond to existing interventions despite adequate adherence to treatments. While novel therapies for SUD are urgently needed, the use of psychedelic drugs for the treatment of SUDs has shown promise. Objectives: This overview of systematic reviews summarizes existing evidence on hallucinogens—serotonergic psychedelics and ketamine—for the treatment of SUD. Methods: A comprehensive search of the literature was conducted to identify relevant evidence for using serotonergic and non-serotonergic psychedelics for the treatment of SUDs. After initial screening (n = 468 studies), 62 studies were retrieved and assessed for eligibility, and a total of 16 systematic reviews were included. Conclusions: Although preliminary, evidence suggests that the use of serotonergic and non-serotonergic psychedelics for the treatment of SUD may provide advantages over traditional therapeutics, and these compounds may eventually become part of the next generation of treatments for SUD under specific circumstances. Research with these drugs has faced significant challenges, though, and caution when interpreting results is warranted, given high risk of bias and several other methodological limitations from the studies to date. Furthermore, risks associated with these drugs are not negligible. For now, the use of psychedelic drugs for the treatment of SUDs remains experimental, and existing evidence is insufficient to support its use in clinical practice. Full article

Background: Healthcare systems globally face complex challenges including rising costs, increasing chronic disease burden, and fragmentation of care. Systems-based models represent promising approaches to healthcare transformation, yet their implementation remains incompletely understood. Objective: To critically analyze the Saudi model of Care (MoC) as a case study of systems-based healthcare transformation, examining its conceptual framework, implementation strategies, and projected health outcomes. Methods: We conducted a narrative review synthesizing publicly available official documents on the Saudi MoC, primarily the 2017 overview and 2025 revision, identified through targeted searches of Ministry of Health websites and grey literature portals (no date restrictions); formal quality appraisal was not applied as sources were official policy documents, with bias mitigated through cross-verification and critical analysis. Results: The Saudi MoC exemplifies systems-based transformation through its multi-layered framework organized around six patient-centered systems of care spanning the lifecycle. Key innovations include: (1) an architectural approach integrating activated individuals, healthy communities, virtual care, and traditional clinical settings; (2) a comprehensive intervention taxonomy with 42 specific initiatives; (3) explicit contextual adaptations for diverse settings; and (4) a phased implementation approach with detailed performance metrics. National indicators improved during the reform period, including life expectancy and maternal and child health. These are national trends observed during the period of health reforms. Causal attribution to the Model of Care requires a counterfactual evaluation. Conclusions: This analysis of the Saudi MoC contributes to the literature on systems-based healthcare transformation by illuminating how theoretical principles can be operationalized at national scale. The model’s patient-centered design, comprehensive intervention taxonomy, and attention to implementation factors offer valuable insights for other healthcare systems pursuing transformation. Further research should examine actual implementation outcomes as the model matures. Full article

To address the challenge of asymmetric deformation and failure in the surrounding rock of main roadways within extra-thick coal seams caused by level differences under intense mining disturbance, this study systematically analyzed the evolution laws of principal stress fields, deviatoric stress fields, and their impact on surrounding rock stability in upper-, middle-, and lower-level roadways within a 20 m extra-thick coal seam during mining retreat. The analysis employed numerical simulation, similarity simulation, and field monitoring. Key findings include the following: ① As the working face advances, the principal stress vector lines deflect following a bias-unloading pattern, while the peak value of the deviatoric stress field (PVDSF) exhibits asymmetric bias-loading characteristics. The lower-layer roadway emerges as the primary load-bearing layer controlling surrounding rock stability. ② The evolution trend of the maximum principal stress vector orientation is consistent across different layers. The deflection trajectory manifests as “the deflection of the goaf side → the near layer orientation → the deflection of the solid coal side”. ③ The deviatoric stress peak zones (DSPZs) at all layers exhibit a characteristic “three-stage” evolution. The deviatoric loading pattern for the lower-layer roadway surrounding rock is the following: initial state double peak region crescent-shaped non-layer distribution type → the range of the bimodal region and the extreme value increased simultaneously, distributed in a non-layer manner → the asymmetrical distribution type of steep drop in the peak area of non-mining deviator stress. ④ The junctions between the mining-side rib and floor and the non-mining-side rib and roof were identified as critical control zones. An innovative zonal asymmetric directional anchoring control technology, “anchor cable foundation support + concrete floor + asymmetric reinforcing anchor cable support”, along with a “One Directional Penetration and Three Synergies” control methodology, was proposed. Field monitoring confirmed the significant effectiveness of the optimized support system. Full article

Background/Objectives: Pyrogens, fever-inducing substances from biological or environmental sources, are recognized by Toll-like receptors (TLRs) predominantly expressed by human monocytes and represent a critical quality attribute (CQA) for pharmaceutical safety. The rabbit pyrogen test (RPT), widely used for pyrogen assessment, suffers from high variability, limited accuracy, and poor reproducibility, particularly for vaccines containing inherent pyrogens such as outer membrane protein complex (OMPC)-based vaccines. Existing in vitro alternatives using peripheral blood mononuclear cells (PBMCs) are challenged by donor-to-donor variability and the operational complexity of ELISA readouts. To support the 3Rs (Refinement, Reduction, Replacement) and provide a more reliable quality control (QC) method, we developed a reporter cell–based monocyte activation test (MAT) suitable for release testing. Methods: We screened human monocytic reporter cell lines engineered with NFκB-responsive promoter elements driving a luminescent reporter. Reporter cells were treated with diverse endotoxin and non-endotoxin pyrogens and luminescence was quantified after stimulation. Selected THP-1-derived reporter cells were used to develop an MAT for OMPC. Assay performance was evaluated following validation guidelines: linearity, accuracy, precision, analytical range (relative to a reference lot), and robustness under deliberate parameter variations. Results: The THP-1 reporter cells could detect a wide range of pyrogens via simple luminescence readouts. For OMPC testing, the MAT demonstrated strong linearity (R² ≥ 0.99), accuracy with relative bias within ±10.3%, and high precision (overall %RSD ≤ 6.9%) across the 25–300% range. Deliberate variations in assay parameters did not materially affect performance, indicating robustness appropriate for routine release testing. Conclusions: The implementation of reporter cell-based MAT assays enhances consistency, reliability, and efficiency in evaluating the pyrogenicity and safety of drug products, supporting global initiatives to minimize animal testing while ensuring regulatory compliance. Full article

Context and Objective: Post-Transcatheter Aortic Valve Replacement (TAVR) mortality exhibits extreme heterogeneity that conventional meta-analyses fail to explain, limiting the clinical utility of evidence synthesis and hindering accurate prognostic assessment. This study evaluated whether meta-learning, using aggregate data from the literature, can predict cohort-level mortality and identify its determinants, overcoming the limitations of traditional methods to provide a clearer understanding of the factors driving TAVR outcomes. Methods: A systematic review following PRISMA guidelines was conducted across five databases. Methodological quality was assessed with standardized tools (Risk of Bias 2, Newcastle-Ottawa Scale, Risk of Bias in Non-randomized Studies of Exposure). After performing conventional meta-analyses and meta-regressions, multiple machine learning models were trained using study-level characteristics as predictors. Advanced optimization with regularization and ensemble techniques was applied to develop a final, optimized model. Results: Fifty-eight studies, encompassing over 533,000 patients, were included. Traditional meta-analysis confirmed extreme heterogeneity (I² = 76.7% in Random Clinical Trials, 96.8% in observational studies), with no explanatory power via meta-regression. The initial AdaBoost model achieved R² = 0.191, outperforming 17 alternative algorithms. Advanced optimization developed a Blend_Optimized model that explained 65.3% of the variability (R² = 0.653), marking a substantial 46 percentage-point increase. Interpretability analysis identified four dominant predictors: Society of Thoracic Surgeons Predicted Risk of Operative Mortality (R² = 0.300), Recruitment Year (R² = 0.212), % Transfemoral (R² = 0.201), and % Diabetes (R² = 0.175), revealing a potent temporal gradient reflecting the evolution of medical practice. Conclusions: Meta-learning significantly surpasses traditional methods in extracting systematic signals from heterogeneous evidence. This study demonstrates that, in addition to patient risk factors, a significant temporal gradient models technological evolution and learning curves. The methodology transforms seemingly unexplained heterogeneity into clinically interpretable patterns, demonstrating the potential of meta-learning as a complementary tool for evidence synthesis in interventional cardiology and opening avenues for applications in other complex cardiovascular fields. Important Limitation: This model predicts cohort-level outcomes and should not be used for individual risk assessment. Full article

Recurrence of the original glomerular disease (GN) poses a significant threat to kidney transplant function and longevity. The probability and severity of this recurrence vary, with C3 glomerulopathy and certain forms of FSGS exhibiting particularly high rates. Kidney transplant GN recurrence risk hinges on the characteristics of the initial GN, recipient/donor genetics, recipient age, donor type, end-stage kidney disease (ESRD) progression rate, and proteinuria levels. Standard immunosuppression has limited efficacy in preventing primary disease recurrence; however, agent selection and induction therapy can influence the risk for specific GNs. Diagnosing recurrent GN involves a comprehensive approach, including clinical evaluation, laboratory tests (such as proteinuria, hematuria, and specific biomarkers like anti-PLA2R for membranous nephropathy or complement for C3G), and, critically, an allograft biopsy analyzed with light, immunofluorescence, and electron microscopy. Treatment strategies are evolving towards targeted therapies, such as rituximab for antibody-mediated GN and complement inhibitors for C3G, moving away from broad immunosuppression. This narrative literature review provides practical monitoring algorithms for post-transplant settings, synthesizing information on the incidence, predictors, diagnostic strategies, and therapeutic options for various glomerular disease subtypes. The methodology involved searching MEDLINE, Embase, and Cochrane databases from 1996 to 2025, prioritizing systematic reviews, cohort studies, registries, and interventional reports. Eligibility criteria included adult transplant recipients and English-language reports on recurrent glomerular disease outcomes, excluding most single-patient case reports. Limitations include potential selection bias, omission of relevant studies, and the absence of a formal risk-of-bias assessment or meta-analysis. The evidence base is heterogeneous, with inconsistent outcome reporting and scarce randomized controlled trials. Future efforts should focus on developing predictive biomarkers, standardizing diagnostic and response criteria, conducting multicenter prospective cohorts and pragmatic trials, and creating shared registries with harmonized data. Full article

Background/Objectives: The COVID-19 pandemic has significantly disrupted healthcare systems worldwide, exposing longstanding weaknesses, particularly in the prevention and control of healthcare-associated infections (HAIs). Regional disparities in infection prevention and control (IPC) strategies offered valuable lessons for improving public health preparedness. This systematic review aims to identify and compare regional IPC approaches adopted during and after the pandemic, highlighting best practices to strengthen healthcare resilience. Methods: The review was conducted in line with PRISMA guidelines and registered in the PROSPERO database (CRD420251032525). Articles published between 1 January 2020 and 31 March 2025, were retrieved from PubMed, Scopus, and Web of Science. Only full-text studies in English were included. The risk of bias was assessed using the ROBINS-I tool. Results: Of the 63 articles initially identified, 8 met the inclusion criteria. The selected studies demonstrated substantial variability in the implementation of IPC. The availability of infrastructure, funding, coordination capacity, and training of medical staff had a significant impact on outcomes. In regions with well-defined protocols and a solid infrastructure, there was a significant decrease in HAIs, while in resource-poor areas, there was a significant increase. Effective measures included continuous monitoring, regular staff training, provision of adequate equipment, expansion of testing capacity, reorganisation of hospitals, and introduction of technological innovations in healthcare. Conclusions: COVID-19 emphasised the importance of adaptable IPC frameworks. Strengthening health systems requires context-specific standards, sustained investment in infrastructure, continuous training, and increased international cooperation to better prepare for future health emergencies. Full article