Tag: nevin manimala

J Formos Med Assoc. 2026 Mar 23:S0929-6646(26)00260-3. doi: 10.1016/j.jfma.2026.03.090. Online ahead of print.

ABSTRACT

BACKGROUND: White matter injury (WMI) is a major cause of neurodevelopmental impairment (NDI) in extremely preterm infants, with ventriculomegaly (VM) and impaired corpus callosum (CC) growth proposed as early indicators of diffuse WMI. This study evaluated whether early cranial ultrasound (cUS) markers are associated with later NDI.

METHODS: This retrospective study included infants born <28 weeks’ gestation and admitted to a tertiary NICU between 2019 and 2020 who completed serial cUS and neurodevelopmental assessments at 18-24 months of corrected age. Those with major brain lesions, severe intraventricular hemorrhage, or congenital anomalies were excluded. cUS measured CC thickness, length, and ventricular width from birth to term-equivalent age (TEA). Clinical characteristics and cUS findings were compared between infants with and without NDI.

RESULTS: Among 70 extremely preterm infants (mean GA 25.7 weeks; BW 827 g), 20 developed NDI. Perinatal factors or comorbidities were similar between groups. Isolated VM at TEA was more frequent in the NDI group (15% vs. 6%), though not statistically significant (p = 0.224). The NDI group had significantly thinner CC at TEA (1.6 mm vs. 1.8 mm, p = 0.026) and slower CC thickness growth rate before TEA (0.01 mm/week vs. 0.02 mm/week, p = 0.019), with no difference in CC length or its progression.

CONCLUSIONS: Serial cUS markers, particularly reduced CC thickness and slower CC growth velocity, were associated with later NDI. Higher but underpowered incidence of isolated VM was observed, supporting the role of serial cUS in early risk stratification over standalone prediction.

PMID:41876310 | DOI:10.1016/j.jfma.2026.03.090

J Prosthet Dent. 2026 Mar 23:S0022-3913(26)00162-9. doi: 10.1016/j.prosdent.2026.03.005. Online ahead of print.

ABSTRACT

STATEMENT OF PROBLEM: Although cracked teeth have been a prevalent clinical concern, a consensus on appropriate treatment strategies-particularly for early-stage cracks without clinical symptoms-remains lacking. Current guidelines do not specify the critical crack dimensions, in terms of depth and width, at which tooth structural integrity becomes significantly compromised, leading to uncertainty in clinical decision-making between monitoring and restorative intervention.

PURPOSE: The purpose of this study was to investigate the effects of crack depth and width on the ultimate strength and stress distribution of human first premolars to guide clinical treatment decisions.

MATERIAL AND METHODS: Forty extracted, sound human first premolars were divided into 5 groups: a control group (no crack) and 4 groups with experimental mesio-occluso-distal cracks of varying depth (D:2 to 4 mm) and width (W:0.5 to 1 mm). Compression tests were conducted to assess ultimate strength, fracture origin, and propagation direction. Statistical analyses were performed using 1-way ANOVA and multiple linear regression (α=.05). Finite element analysis (FEA) was used to simulate stress distribution based on both principal stress and energy-based failure theories. The computational results were validated with experimental findings. Smaller cracks (D:1 to 3 mm; W:0.1 to 0.5 mm) were modeled via FEA to identify subclinical critical thresholds.

RESULTS: Crack depth dominated tooth ultimate strength reduction (β=-.803, P<.001), while width became insignificant at a depth of 4 mm (β=-.059, P=.480). Fractures in cracked teeth originated at crack tips (91% of cases), whereas fractures in sound teeth occurred at the palatal cusp. The von Mises stress criterion accurately predicted the behavior of cracked teeth, unlike the principal stress theory. Critical crack size thresholds were identified: a 15% strength reduction in the D1W0.5 and D2W0.1 cases and a 67% strength reduction (relative to sound teeth) when cracks extended to the pulp chamber in the D3W1 case.

CONCLUSIONS: Increasing crack size significantly weakens tooth strength, with depth being the dominant factor. The von Mises stress theory is recommended for analyzing cracked teeth. The identified critical crack sizes provide actionable thresholds for restorative intervention.

PMID:41876301 | DOI:10.1016/j.prosdent.2026.03.005

ISA Trans. 2026 Mar 20:S0019-0578(26)00154-0. doi: 10.1016/j.isatra.2026.03.033. Online ahead of print.

ABSTRACT

This paper proposes a reinforcement learning (RL)-based adaptive covariance scaling framework for robust INS/UWB integrated navigation in dynamic and NLOS-prone environments. By formulating covariance tuning as a Partially Observable Markov Decision Process (POMDP) and employing a recurrent PPO policy, the method enables anchor-wise adjustment of the UWB measurement noise to balance accuracy and statistical consistency. Simulation results show that the proposed approach achieves an RMSE of 0.258m, outperforming classical adaptive filters and existing RL baselines. Real-world quadrotor experiments further demonstrate centimeter-level accuracy (0.036m RMSE) and strong robustness under severe NLOS and anchor dropout conditions, highlighting its effectiveness for resilient intelligent navigation systems.

PMID:41876298 | DOI:10.1016/j.isatra.2026.03.033

Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2026 Jan;38(1):166-171. doi: 10.3760/cma.j.cn121430-20250319-00278.

ABSTRACT

OBJECTIVE: Predict the occurrence of pressure injury in critically ill patients by using machine learning models and conducting internal validation.

METHODS: A prospective cohort study was conducted. Critically ill patients admitted to the intensive care unit (ICU) of the North District of Hangzhou First People’s Hospital from January 2023 to March 2024 were enrolled using convenience sampling. Patients were divided into two groups based on the occurrence of pressure injury during their ICU stay, and the differences in pressure injury related indicators were compared between the groups. The dataset was randomly divided into a training set (75%) and a validation set (25%). Feature selection was performed using the Lasso regression. Independent risk factors were then identified via multivariate Logistic regression analysis. An extreme gradient boosting (XG-Boost) machine learning model was developed to predict pressure injury risk. The model’s performance was comprehensively evaluated using receiver operator characteristic curve (ROC curve), calibration curve, and clinical decision curve analysis (DCA). The Shapley Additive exPlanations (SHAP) method was used to rank feature importance.

RESULTS: A total of 350 critically ill patients were included, of whom 102 (29.1%) developed pressure injuries. There were statistically significant differences in consciousness status, mechanical ventilation, sedative use, length of ICU stay, Braden score, use of warm blankets, white blood cell count, neutrophil count, blood glucose, and lactate level between the pressure injury and non-pressure injury groups (all P<0.05). Lasso regression analysis identified six predictive variables: consciousness status, mechanical ventilation, use of warm blankets, length of ICU stay, neutrophil count, and blood glucose. Multivariate Logistic regression analysis subsequently revealed that mechanical ventilation, use of warm blankets, prolonged ICU stay, elevated neutrophil count, and elevated blood glucose were independent risk factors for pressure injuries [mechanical ventilation: odds ratio (OR)=2.338, 95% confidence interval (95%CI) was 1.768-3.089, P=0.002; use of warm blankets: OR=1.772, 95%CI was 1.341-2.338, P=0.039; prolonged ICU stay: OR=1.081, 95%CI was 1.067-1.097, P<0.001; elevated neutrophil count: OR=1.044, 95%CI was 1.022-1.067, P=0.036; elevated blood glucose: OR=1.062, 95%CI was 1.031-1.094, P=0.027]. Based on these six risk factors, a predictive model was constructed using the XG-Boost method. The ROC curve analysis demonstrated the model has high predictive performance, with an area under the curve (AUC) of 0.896 (95%CI was 0.863-0.929) in the training set and 0.835 (95%CI was 0.761-0.908) in the validation set. The calibration curve indicated good agreement between predicted probabilities and actual outcomes. DCA further suggested that the model had clinical utility across a wide range of threshold probabilities. SHAP analysis ranked feature importance in descending order as follows: length of ICU stay, mechanical ventilation, neutrophil count, consciousness status, blood glucose, and use of warm blankets.

CONCLUSIONS: The constructed XG-Boost machine learning model has high performance in predicting the occurrence of pressure injury in critically ill patients. Identify key predictive factors can aid clinical risk assessment and intervention.

PMID:41876243 | DOI:10.3760/cma.j.cn121430-20250319-00278

Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2026 Jan;38(1):146-151. doi: 10.3760/cma.j.cn121430-20250319-00163.

ABSTRACT

OBJECTIVE: To investigate the factors influencing the apnea test (AT) and its clinical effects in brain death determination under updated criteria, and to provide evidence for optimizing and reducing the risk of false-negative results and complications.

METHODS: Based on the data from the Anhui Provincial Brain Injury Evaluation Quality Control Center, the data of brain-dead patients who completed AT with an off ventilator duration of 5-11 minutes were analyzed retrospectively. Data from January 2018 to March 2025 were used as the model development cohort, and the data from June to December 2025 were used as the external validation cohort. Demographic characteristics, clinical data, evaluation and examination indicators, AT operation details, etc. were extracted using standardized case report form. Temporal trends of AT positive rate and the incidences of severe hypercapnia, acidosis, hypoxemia and other complications when offline for 5-11 minutes were evaluated using the Cochran-Armitage trend test, and the key factors affecting the change of arterial partial pressure of carbon dioxide (PaCO₂) and pH were analyzed by multiple linear regression model.

RESULTS: The model development cohort included 384 patients with brain death, and the external validation cohort included 47 patients with brain death. There was no significant difference in baseline characteristics between the two cohorts (all P>0.05). With the extension of offline time, the positive rate of AT was gradually increased (Cochran-Armitage trend test: Z=3.52, P<0.001), rising from 76.5% (13/17) at 5 minutes to 91.7% (11/12) at 7 minutes, and plateaued after 7 minutes. The trend analysis of complications in the same period showed that the incidence of severe hypercapnia (PaCO₂>80 mmHg, 1 mmHg=0.133 kPa) showed a significant increasing trend (Z=4.09, P<0.001), and was higher at 10 minutes than at 9 minutes [44.7% (59/132) vs. 21.6% (8/37), P<0.05]. Severe acidosis (pH<7.20) became more frequent over time (Z=-4.69, P<0.001), and was higher at 10 minutes than at 7 minutes [73.5% (97/132) vs. 58.3% (7/12), P<0.05]. The incidence of hypoxemia [arterial partial pressure of oxygen (PaO₂) <60 mmHg] showed a decreasing trend (Z=-5.21, P<0.001), with no statistically significant difference in incidence between 7-11 minutes (F=0.859, P=0.525). The prediction model was established by multiple regression, indicated that offline time, pre-AT PaCO₂, pre-AT pH, heart rate, and body weight collectively influenced post-AT PaCO₂ (R²=0.284, P<0.001). Offline time, pre-AT pH, heart rate, and hemoglobin were associated with post-AT pH (R²=0.455, P<0.001). External validation indicated good performance for the pH model (mean absolute error was 0.038, R²=0.69) and acceptable performance for the PaCO₂ model (mean absolute error was 6.21 mmHg, R²=0.62).

CONCLUSIONS: When implementing the dual-criteria standard (PaCO₂ and pH), an offline time window of 7 to 9 minutes can balance diagnostic efficacy for brain death with patient safety. Pre-intervention strategies, such as lowering pH or raising PaCO₂ before disconnection, may shorten the time needed to reach AT targets. However, should be guided by a comprehensive assessment of individualized patient factors, including baseline pH, PaCO₂, heart rate, hemoglobin, and body weight.

PMID:41876240 | DOI:10.3760/cma.j.cn121430-20250319-00163

Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2026 Jan;38(1):131-137. doi: 10.3760/cma.j.cn121430-20250102-00004.

ABSTRACT

OBJECTIVE: To identify the risk factors for the progression of severe pneumonia to acute respiratory distress syndrome (ARDS) and to construct a prediction model based on the random forest algorithm, providing a basis for disease assessment, early intervention, and prognosis improvement in severe pneumonia.

METHODS: A retrospective observational study was conducted. Patients with severe pneumonia admitted to the intensive care unit (ICU) of the Second Affiliated Hospital of Zunyi Medical University from January 2020 to May 2024 were enrolled. Data including general patient information, vital signs, blood test results, disease assessment indicators within 24 hours of ICU admission, and outcome measures were collected. Patients were divided into ARDS group and non-ARDS group according to whether they progressed to ARDS. Univariate Logistic regression analysis was used to screen the risk factors for the progression of severe pneumonia to ARDS, and a random forest based prediction model was constructed. Model performance and stability were validated using 1 000 resampling iterations.

RESULTS: A total of 181 severe pneumonia patients were included, of whom 73 progressed to ARDS, with an incidence rate of 40.3%. Compared to the non-ARDS group, the ARDS group had significantly lower lowest systolic blood pressure, lowest diastolic blood pressure, lowest oxygenation index, pH value, and albumin level, while showing significantly higher maximum activated partial thromboplastin time (APTT), Acute Physiology and Chronic Health Evaluation (APACHE), and Lung Injury Prediction Score (LIPS; all P<0.05). There were no statistically significant differences in other baseline data comparisons (all P>0.05). Univariate Logistic regression analysis showed that pH, lowest systolic blood pressure, albumin, APACHE score, and LIPS score were risk factors for the progression to ARDS in severe pneumonia patients [pH: odds ratio (OR)=0.04, 95% confidence interval (95%CI) was 0.00-0.96, P=0.047, cut-off was 7.34; lowest systolic blood pressure: OR=0.98, 95%CI was 0.97-1.00, P=0.044, cut-off was 90 mmHg (1 mmHg=0.133 kPa); albumin: OR=0.94, 95%CI was 0.89-0.99, P=0.032, cut-off was 28.05 g/L; APACHE score: OR=1.08, 95%CI was 1.02-1.14, P=0.008, cut-off was 23; LIPS: OR=1.37, 95%CI was 1.09-1.72, P=0.007, cut-off was 5]. A random forest model constructed with these risk factors ranked the importance of the indicators from high to low as follows: pH, lowest systolic blood pressure, albumin, APACHE score, and LIPS (with Gini Index of 31.08, 30.74, 29.35, 28.01, and 24.92, respectively). Validation with 1 000 bootstrap resamplings showed that the model had an area under the receiver operator characteristic curve (AUC) of 0.909 (95%CI was 0.870-0.943), a sensitivity of 0.823 (95%CI was 0.699-0.932), and a specificity of 0.869 (95%CI was 0.741-0.963).

CONCLUSIONS: pH<7.34, lowest systolic blood pressure<90 mmHg, albumin<28.05 g/L, APACHE>23, and LIPS>5 are risk factors for the progression of severe pneumonia to ARDS. The model constructed based on these factors using the random forest algorithm can effectively predict whether severe pneumonia patients will progress to ARDS.

PMID:41876238 | DOI:10.3760/cma.j.cn121430-20250102-00004

Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2026 Jan;38(1):117-123. doi: 10.3760/cma.j.cn121430-20250623-00345.

ABSTRACT

OBJECTIVE: To investigate the relationship between the pathogen load kinetics detected by droplet digital polymerase chain reaction (ddPCR) and the prognosis of patients with suspected bloodstream infection (BSI).

METHODS: A prospective observational study was conducted. Patients aged >18 years with suspected BSI admitted to intensive care unit department of Zhejiang Provincial People’s Hospital from March 1, 2022 to October 31, 2023 were consecutively enrolled. Patients with blood ddPCR detection positive for pathogenic bacteria such as Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, or Pseudomonas aeruginosa were included. Demographics, clinical profile, vital signs, and comorbidities data of patients upon admission were collected. Blood ddPCR testing, laboratory tests, and disease severity scoring were performed on days 0, 3, and 7. Based on whether the follow-up ddPCR test at day 7 showed a ≥50% decrease in all pathogen load compared to admission, patients were divided into the pathogen load decrease group and pathogen load non-decrease group. Baseline characteristics and the dynamic changes in perfusion indicators, inflammation indicators, and disease severity scores between two groups were compared. The correlation between ddPCR detection of pathogen load and perfusion indicators, inflammation indicators, and disease severity scores were analyzed using Spearman test. The 28-day cumulative survival rate of pathogen load decrease group and pathogen load non-decrease group was analyzed using Kaplan-Meier survival curve. Patients were divided into the survival group and death group according to the 28-day prognosis, and the clinical data were compared between the two groups. Independent risk factors for 28-day mortality in patients with suspected BSI were identified using multivariate Cox proportional hazards regression models.

RESULTS: A total of 189 suspected BSI patients with ddPCR positive for Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, or Pseudomonas aeruginosa infection were enrolled, of whom 121 underwent dynamic monitoring. Among these 121 patients, 82 showed a decrease in ddPCR pathogen load at day 7, while 39 did not. During the 28-day follow-up, 76 survived and 45 died, and the 28-day mortality was 37.2%. Compared to the pathogen load decrease group, the pathogen load non-decrease group had a higher proportion of patients receiving vasoactive drug support and mechanical ventilation, and a higher 28-day mortality (all P<0.05). In patients with suspected BSI, the ddPCR pathogen load on day 0 showed significant positive correlations with high-sensitivity C-reactive protein (hs-CRP), procalcitonin (PCT), blood lactate (Lac), Acute Physiology and Chronic Health Evaluation II (APACHE II), and Sequential Organ Failure Assessment (SOFA; r values were 0.150, 0.273, 0.370, 0.334, 0.311, respectively; all P<0.05). The Lac, hs-CRP, PCT and APACHE II in pathogen load decrease group decreased with time. However, hs-CRP and PCT did not decrease in pathogen load non-decrease group, and APACHE II and SOFA scores further increased. At day 7, compared to the pathogen load non-decrease group, the pathogen load decrease group showed significant reductions in hs-CRP, PCT, APACHE II and SOFA scores (all P<0.05); however, there was still no statistically significant difference in Lac between the two groups. Kaplan-Meier survival curve analysis showed that the 28-day cumulative survival rate of the pathogen load decrease group was significantly higher than that of the pathogen load non-decrease group (Log-rank test: χ ²=5.969, P=0.015). Compared to the survival group, the death group was older, had higher SOFA score, lower platelet count (PLT) and higher total pathogen load detected by ddPCR at day 7, and had a higher proportion receiving continuous renal replacement therapy (CRRT) and a higher proportion belonging to the non-decreased pathogen load. Multivariate Cox regression analysis showed that increasing age [hazard ratio (HR)=1.048, 95% confidence interval (95%CI) was 1.020-1.078, P<0.001], higher SOFA score (HR=1.127, 95%CI was 1.027-1.235, P=0.007), and pathogen load non-decrease (HR=2.165, 95%CI was 1.148-4.091, P=0.017) were independent risk factors for 28-day mortality in patients with suspected BSI.

CONCLUSIONS: Dynamic monitoring of pathogen load changes by ddPCR can reflect the prognosis of patients with suspected BSI, suggesting the role of ddPCR detection in therapeutic monitoring for patients with BSI.

PMID:41876236 | DOI:10.3760/cma.j.cn121430-20250623-00345

JMIR Mhealth Uhealth. 2026 Mar 24;14:e70192. doi: 10.2196/70192.

ABSTRACT

BACKGROUND: Childhood obesity and unhealthy dietary habits remain major public health concerns and are influenced by the surrounding food environment. Food marketing, particularly for ultraprocessed foods (UPFs), shapes children’s food preferences and consumption. However, food environments are complex and constantly changing, making them difficult to map and monitor. Developing approaches that capture these dynamics is essential to understand and address children’s exposure to unhealthy food marketing.

OBJECTIVE: This study aimed to pilot-test a novel tool consisting of a smartphone app and dashboard designed to identify areas where children are exposed to outdoor food advertising. Additionally, the study assessed the prevalence of advertisements for UPFs, health-promoting foods, and offers in the identified areas and explored differences in exposure by city size and socioeconomic status (SES).

METHODS: A cross-sectional study was performed in 2 Swedish counties. Initially, 46 children from 4 schools in areas with varying SES used a smartphone app to take pictures of food advertisements that they encountered in their everyday lives. The app also recorded the GPS locations of where the pictures were taken. Pictures with associated GPS data were automatically uploaded and visualized in a web-based dashboard, allowing for identification of areas where children see many food advertisements, so-called “hotspot areas.” The identified hotspot areas were subsequently visited by 2 researchers (SS and PF), who systematically photographed all food advertisements in the areas. All pictures taken by the researchers were later analyzed based on their content of UPFs, health-promoting foods such as fruits, berries, vegetables, and seafood (FBVS), and price promotions.

RESULTS: Based on 1308 pictures of outdoor food advertisements taken by children using the app, 34 hotspot areas were identified through the dashboard. In these areas, researchers collected 2955 pictures of outdoor food advertisements during the mapping activity. Overall, 77.5% (2291/2955) of advertisements promoted UPFs, with no significant difference between the large and small cities. In Stockholm, a higher proportion of UPFs appeared in the high-SES area compared to the low-SES area, though the proportion was high in both areas. FBVS featured in 20.8% (616/2955) of advertisements, slightly more often in Stockholm and in the high-SES area. Price promotions appeared in 23.6% (697/2955) of advertisements, mainly featured UPFs (518/697, 74.3%) and less often FBVS (142/697, 20.4%). Price promotions for UPFs were somewhat more frequent in Stockholm, and FBVS promotions were more common in the high-SES area.

CONCLUSIONS: Using a novel, child-centered, mobile health tool combining a smartphone app and dashboard, this study identified local food advertising hotspots. Most advertisements in the hotspots promoted UPFs, while a few featured FBVS, a pattern also reflected in price promotions. These trends were consistent across areas, highlighting a food marketing landscape misaligned with dietary guidelines and the need for policy action.

PMID:41876212 | DOI:10.2196/70192

BMJ Support Palliat Care. 2026 Mar 24:spcare-2025-006013. doi: 10.1136/spcare-2025-006013. Online ahead of print.

ABSTRACT

OBJECTIVES: To describe longitudinal changes in quality of life (QOL) and symptoms among patients with acute myeloid leukaemia (AML) receiving real-world early palliative care (EPC) during the first year after diagnosis.

METHODS: This prospective observational study enrolled consecutive adults with AML followed in an outpatient EPC clinic. QOL and symptoms were assessed monthly using the Functional Assessment of Cancer Therapy-Leukemia (FACT-Leu), the Edmonton Symptom Assessment Scale (ESAS) and the Hospital Anxiety and Depression Scale (HADS). Scores were analysed through joint modelling, integrating longitudinal and survival data, and sensitivity analyses.

RESULTS: Thirty-eight patients contributed 169 FACT-Leu, 151 ESAS and 111 HADS questionnaires. From baseline, median FACT-Leu scores improved from 108.7 to 135.7 at 4 months and remained stable through 8 and 12 months (p≤0.011), while ESAS scores decreased from 25.2 to 5.7 by 4 months and remained low through 12 months (p<0.001), indicating sustained symptom improvement. HADS scores showed no statistically significant changes, although a modest anxiety improvement was noted. Trajectories remained consistent across all sensitivity analyses.

CONCLUSIONS: In AML patients receiving EPC in a real-world outpatient setting, QOL and symptom burden showed sustained improvement over time. These descriptive findings highlight the potential effectiveness and clinical relevance of EPC in routine AML care and provide real-world reference data for future controlled studies.

PMID:41876210 | DOI:10.1136/spcare-2025-006013

Mol Oncol. 2026 Mar 24. doi: 10.1002/1878-0261.70242. Online ahead of print.

ABSTRACT

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults, with poor prognosis despite multimodal therapy. Chloride cotransporters NKCC1 and KCC2 are key regulators of intracellular chloride levels and thereby determine whether GABA acts inhibitory or excitatory. In GBM, disrupted chloride homeostasis promotes proliferation, migration, and stem-like properties, but its clinical relevance is not fully understood. We analyzed NKCC1 and KCC2 expression in GBM samples, considering clinical parameters, such as age, gender, and MGMT promoter methylation. Statistical analyses included ROC-based cutoff determination, Kaplan-Meier survival analysis, and subgroup. Immunohistochemistry was performed to identify cell types expressing NKCC1. NKCC1 expression was significantly higher in older patients and emerged as a prognostic marker for recurrence-free survival, with lower levels correlating with delayed recurrence, although overall survival was unaffected. NKCC1 was expressed in stem-like, astrocytic, and neuronal progenitor cells, but not in mature neurons. These findings identify NKCC1 as a regulator of GBM progression and recurrence, linking chloride transporter imbalance to GABAergic signaling. Targeting NKCC1 and restoring chloride homeostasis may provide promising new treatment strategies.

PMID:41876207 | DOI:10.1002/1878-0261.70242