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Respiratory Syncytial Virus (RSV) is one of the primary pathogen responsible for severe lower respiratory tract infections in preterm infants, placing a significant burden on patients, their families, and society. Nirsevimab, a recently developed RSV monoclonal antibody, has demonstrated promising efficacy in this population according to preliminary studies. However, there remains a need for comprehensive systematic reviews and meta-analyses to evaluate the effectiveness of nirsevimab in preventing RSV-related lower respiratory tract infections in preterm infants. A search of the PubMed and EMBASE databases was conducted to identify randomized controlled trials (RCTs) and observational studies assessing the prevention of RSV infection in preterm infants using nirsevimab. Relevant data were extracted and subjected to meta-analysis. Five studies involving a total of 7,347 preterm infants (3,987 in the nirsevimab group and 3,360 in the control group) were included. The meta-analysis revealed that nirsevimab significantly reduced the incidence of medically attended RSV-associated lower respiratory tract infections (OR = 0.25; 95% CI: 0.15, 0.40; P < 0.0001) and hospitalizations due to RSV-associated lower respiratory tract infections (OR = 0.27; 95% CI: 0.19, 0.38; P < 0.0001). Nirsevimab significantly decreases the risk of RSV-related infection in preterm infants and represents a valuable intervention for RSV prevention in this vulnerable population. https://www.crd.york.ac.uk/prospero/, identifier CRD42025629937.

Autonomous artificial intelligence (AI) models for deciding treatment strategies are available but rarely applied prospectively in clinical settings. Here we present a prospective study of deploying daGOAT, an algorithm we have developed, as a conditional autonomous AI agent to prescribe a drug to prevent severe (grade 3−4) acute graft-versus-host disease (acute GvHD) following human leukocyte antigen (HLA)-mismatched haematopoietic cell transplantation (ClinicalTrials.gov, NCT05600855). During the enrollment period physicians invite 85% of eligible patients to participate and 88% of the invited patients agree. Among the 110 enrolled participants who receive HLA-haploidentical transplants, daGOAT predicts intermediate to high risk of severe acute GvHD in 57 participants between days +17 and +23 posttransplant and prescribes ruxolitinib in addition to the existing regimen to intensify immune suppression. The initial compliance with AI prescription is 98% (56/57), with dose and/or schedule deviating from the AI prescription within one month in a total of eight participants. In conclusion, we show that many physicians and patients are receptive to using conditional autonomous AI to prescribe a drug and that the decision for pharmaceutical intervention could be facilitated by autonomous AI. Autonomous artificial intelligence (AI) models to replace human decision-making in medical intervention need thorough testing. Here authors present the results of a clinical trial, NCT05600855, in which daGOAT, a conditional autonomous artificial intelligence agent successfully makes the decision whether to prescribe an immune suppressive drug to prevent severe acute graft-versus-host disease following HLA-mismatched haematopoietic cell transplantation.

ObjectiveTo report how the Chinese mainland battled its first omicron wave, which happened in Tianjin, a metropolis with 14 million residents. We also sought to better understand how clinical features affected the timing of viral clearance.DesignA retrospective study of the omicron wave in Tianjin between 8 January 2022 and 3 March 2022.SettingExcept for the first cases on 8 January, all the omicron cases were identified through PCR mass testing in the residential communities. Residential quarantine and serial PCR mass testing were dynamically adjusted according to the trends of new cases.ParticipantsAll the 417 consecutive PCR-positive cases identified through mass screening of the entire city’s 14 million residents. 45.3% of the cases were male, and the median age was 37 (range 0.3–90). 389 (93%) cases had complete data for analysing the correlation between clinical features and the timing of viral clearance.Main outcome and measureTime to viral clearance.ResultsTianjin initiated the ‘dynamic zero-COVID’ policy very early, that is, when daily new case number was ≈0.4 cases per 1 000 000 residents. Daily new cases dropped to <5 after 3 February, and the number of affected residential subdivisions dropped to ≤2 after 13 February. 64% (267/417) of the cases had no or mild symptoms. The median interval from hospital admission to viral clearance was 10 days (range 3–28). An exploratory analysis identified a feature cluster associated with earlier viral clearance, with HRs of 3.56 (95% CI 1.66 to 7.63) and 3.15 (95% CI 1.68 to 5.91) in the training and validation sets, respectively.ConclusionsThe ‘dynamic zero-COVID’ policy can suppress an omicron wave within a month. It might be possible to predict in advance which cases will require shorter periods of isolation based on their clinical features.

Objective: To evaluate the association between induced abortion (IA) and breast cancer risk among Chinese females. Methods: We searched three English databases (PubMed, ScienceDirect, and Wiley) and three Chinese databases (CNKI, WanFang, and VIP) for studies up to December 2012, supplemented by manual searches. Two reviewers independently conducted the literature searching, study selection, and data extraction and quality assessment of included studies. Random effects models were used to estimate the summary odds ratios (ORs) and the 95 % confidence intervals (CIs). Results: A total of 36 articles (two cohort studies and 34 case–control studies) covering 14 provinces in China were included in this review. Compared to people without any history of IA, an increased risk of breast cancer was observed among females who had at least one IA (OR = 1.44, 95 % CI 1.29–1.59, I² = 82.6 %, p < 0.001, n = 34). No significant publication bias was found among the included studies (Egger test, p = 0.176). The risk increased to 1.76 (95 % CI 1.39–2.22) and 1.89 (95 % CI 1.40–2.55) for people who had at least two IAs and at least three IAs, respectively. Subgroup analyses showed similar results to the primary results. Meta-regression analysis of the included studies found that the association between IA and breast cancer risk attenuated with increasing percent of IA in the control group (β = −0.022, p < 0.001). Conclusion: IA is significantly associated with an increased risk of breast cancer among Chinese females, and the risk of breast cancer increases as the number of IA increases. If IA were to be confirmed as a risk factor for breast cancer, high rates of IA in China may contribute to increasing breast cancer rates.

The algorithm for cord blood (CB) unit selection is still somewhat ambiguous. We retrospectively analyzed 620 cases of acute leukemia between 2015 and 2020, who were treated with myeloablative single‐unit umbilical CB transplantation (UCBT). We found that, when human leukocyte antigen (HLA) mismatch was ≤3/10, CD34+ cell dosage <0.83 × 105/kg—considerably lower than prevalent guidelines—was permissible without affecting survival. Moreover, synergy between donor killer‐cell immunoglobulin‐like receptors (KIR) haplotypes‐B and donor‐recipient HLA‐C mismatch protected against relapse‐related mortality. We submit that minimum required CD34+ cell dosage can possibly be relaxed to broaden access to UCBT, and donor KIR genotyping should be considered during unit selection.

ETV6::RUNX1 is the most common fusion gene in childhood acute lymphoblastic leukaemia (ALL) and is associated with favorable outcomes, especially in low‐risk children. However, as many as 10% of children relapse within 3 years, and such early relapses have poor survival. Identifying children at risk for early relapse is an important challenge. We interrogated data from 87 children with low‐risk ETV6::RUNX1‐positive B‐cell ALL and with available preserved bone marrow samples (discovery cohort). We profiled somatic point mutations in a panel of 559 genes and genome‐wide transcriptome and single‐nucleotide variants. We found high TIMD4 expression (> 85th‐percentile value) at diagnosis was the most important independent prognostic factor of early relapse (hazard ratio [HR] = 5.07 [1.76, 14.62]; p = 0.03). In an independent validation cohort of low‐risk ETV6::RUNX1‐positive B‐cell ALL (N = 68) high TIMD4 expression at diagnosis had an HR = 4.78 [1.07, 21.36] (p = 0.04) for early relapse. In another validation cohort including 78 children with low‐risk ETV6::RUNX1‐negative B‐cell ALL, high TIMD4 expression at diagnosis had an HR = 3.93 [1.31, 11.79] (p = 0.01). Our results suggest high TIMD4 expression at diagnosis in low‐risk B‐cell ALL in children might be associated with high risk for early relapse.

To overcome the limitation of artificial judgment of meibomian gland morphology, we proposed a solution based on an improved fuzzy c-means (FCM) algorithm and rough sets theory. The rough sets reduced the redundant attributes while ensuring classification accuracy, and greatly reduced the amount of computation to achieve information dimension compression and knowledge system simplification. However, before this reduction, data must be discretized, and this process causes some degree of information loss. Therefore, to maintain the integrity of the information, we used the improved FCM to make attributes fuzzy instead of discrete before continuing with attribute reduction, and thus, the implicit knowledge and decision rules were more accurate. Our algorithm overcame the defects of the traditional FCM algorithm, which is sensitive to outliers and easily falls into local optima. Our experimental results show that the proposed method improved recognition efficiency without degrading recognition accuracy, which was as high as 97.5%. Furthermore, the meibomian gland morphology was diagnosed efficiently, and thus this method can provide practical application values for the recognition of meibomian gland morphology.