Respiratory tract infections (RTIs) represent one of the main health problems in children. Probiotics are viable bacteria that colonize the intestine and affect the host intestinal microbial balance. Accumulating evidence suggests that probiotic consumption may decrease the incidence of or modify RTIs. The authors systematically reviewed data from randomized controlled trials (RCTs) to investigate the effect of probiotic consumption on RTIs in children. MEDLINE/PubMed, Embase, Cochrane Library, and Web of Science were systematically searched for RCTs regarding the effect of probiotics on RTIs in children. The outcomes included number of children experienced with at least 1 RTI episode, duration of illness episodes, days of illness per subject, and school/day care absenteeism due to infection. A random-effects model was used to calculate pooled relative risks, or mean difference (MD) with the corresponding 95% confidence interval (CI). A total of 23 trials involving 6269 children were eligible for inclusion in the systematic review. None of the trials showed a high risk of bias. The quality of the evidence of outcomes was moderate. The age range of subjects was from newborn to 18 years. The results of meta-analysis showed that probiotic consumption significantly decreased the number of subjects having at least 1 RTI episode (17 RCTs, 4513 children, relative risk 0.89, 95% CI 0.82-0.96, P = 0.004). Children supplemented with probiotics had fewer numbers of days of RTIs per person compared with children who had taken a placebo (6 RCTs, 2067 children, MD -0.16, 95% CI -0.29 to 0.02, P = 0.03), and had fewer numbers of days absent from day care/school (8 RCTs, 1499 children, MD -0.94, 95% CI -1.72 to -0.15, P = 0.02). However, there was no statistically significant difference of illness episode duration between probiotic intervention group and placebo group (9 RCTs, 2817 children, MD -0.60, 95% CI -1.49 to 0.30, P = 0.19). Based on the available data and taking into account the safety profile of RCTs, probiotic consumption appears to be a feasible way to decrease the incidence of RTIs in children.

Objective: The objective of this study is to explore the association between patient-centered care (PCC) and inpatient healthcare outcomes, including self-reported physical and mental health status, subjective necessity of hospitalization, and physician-induced demand behaviors.Methods: A cross-sectional survey was conducted to assess patient-centered care among inpatients in comprehensive hospitals through QR codes after discharge from September 2021 to December 2021 and had 5,222 respondents in Jiayuguan, Gansu. The questionnaire included a translated 6-item version of the PCC questionnaire, physician-induced behaviors, and patients' sociodemographic characteristics including gender, household registration, age, and income. Logistic regression analyses were conducted to assess whether PCC promoted self-reported health, the subjective necessity of hospitalization, and decreased physician-induced demand. The interactions between PCC and household registration were implemented to assess the effect of the difference between adequate and inadequate healthcare resources.Results: PCC promoted the patient's self-reported physical (OR = 4.154, p < 0.001) and mental health (OR = 5.642, p < 0.001) and subjective necessity of hospitalization (OR = 6.160, p < 0.001). Meanwhile, PCC reduced physician-induced demand in advising to buy medicines outside (OR = 0.415, p < 0.001), paying at the outpatient clinic (OR =0.349, p < 0.001), issuing unnecessary or repeated prescriptions and medical tests (OR = 0.320, p < 0.001), and requiring discharge and readmitting (OR = 0.389, p < 0.001).Conclusion: By improving health outcomes for inpatients and reducing the risk of physician-induced demand, PCC can benefit both patients and health insurance systems. Therefore, PCC should be implemented in healthcare settings.

Background: Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. Objective: The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. Methods: An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. Results: The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. Conclusions: User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

Precise classification of non-synonymous single nucleotide variants (SNVs) is a fundamental goal of clinical genetics. Next-generation sequencing technology is effective for establishing the basis of genetic diseases. However, identification of variants that are causal for genetic diseases remains a challenge. We analyzed human non-synonymous SNVs from a multilevel perspective to characterize pathogenicity. We showed that computational tools, though each having its own strength and weakness, tend to be overly dependent on the degree of conservation. For the mutations at non-degenerate sites, the amino acid sites of pathogenic substitutions show a distinct distribution in the classes of protein domains compared with the sites of benign substitutions. Overlooked disease susceptibility of genes explains in part the failures of computational tools. The more pathogenic sites observed, the more likely the gene is expressed in a high abundance or in a high tissue-specific manner, and have a high node degree of protein-protein interaction. The destroyed functions due to some false-negative mutations may arise because of a reprieve from the epigenetic repressed state which shouldn’t happen in multiple biological conditions, instead of the defective protein. Our work adds more to our knowledge of non-synonymous SNVs’ pathogenicity, thus will benefit the field of clinical genetics.

The Prechtl General Movements Assessment (GMA) is increasingly recognized for its role in evaluating the integrity of the developing nervous system and predicting motor dysfunctions, particularly in conditions such as cerebral palsy (CP). However, the necessity for highly trained professionals has hindered the adoption of GMA as an early screening tool in some countries. In this study, we propose a deep learning-based motor assessment model (MAM) that combines infant videos and basic characteristics, with the aim of automating GMA at the fidgety movements (FMs) stage. MAM demonstrates strong performance, achieving an Area Under the Curve (AUC) of 0.967 during external validation. Importantly, it adheres closely to the principles of GMA and exhibits robust interpretability, as it can accurately identify FMs within videos, showing substantial agreement with expert assessments. Leveraging the predicted FMs frequency, a quantitative GMA method is introduced, which achieves an AUC of 0.956 and enhances the diagnostic accuracy of GMA beginners by 11.0%. The development of MAM holds the potential to significantly streamline early CP screening and revolutionize the field of video-based quantitative medical diagnostics.