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Introduction: Pneumococcal disease leads to high morbidity and mortality, particularly in older adults and immunocompromised individuals. Many pneumococcal conjugated vaccines (PCVs) have become available. However, the immunogenicity, efficacy, and effectiveness data of these vaccines in older adults and immunocompromised individuals are limited.

Areas covered: This review aims to critically examine the immune responses, immune correlations, efficacy, real-world effectiveness, and cost-effectiveness of pneumococcal conjugated vaccines (PCVs) in older adults and immunocompromised individuals.

Expert opinion: A single dose of 20-valent or 21-valent PCV is recommended for older adults and immunocompromised individuals. Immune correlates of protection vary by serotype and race. An IgG level of 0.35 µg/mL is associated with protection, though this threshold is serotype-dependent. Opsonophagocytic assays, with a threshold of 1:8, remain the most reliable functional correlate of protection against invasive pneumococcal disease. Standardized immunological assays are essential for evaluating immune responses. High-valent PCVs have shown noninferior immunogenicity compared to PCV13, though geometric mean fold rises (GMFRs) for shared serotypes are slightly lower. Real-world effectiveness data are still needed, particularly in regions with differing serotype prevalence. Serotype surveillance is crucial when introducing PCV programs. Due to the high cost of higher-valent PCVs, many countries continue using PCV13 or PCV15 followed by PPSV23 for high-risk groups.

Aims: This study aimed to assess healthcare professionals' attitudes toward accreditation and compare perceptions between staff working in Joint Commission International (JCI)-accredited and nationally accredited medical institutions in Astana, Kazakhstan.

Methods: A cross-sectional survey conducted among health professionals at a JCI-accredited hospital and two nationally accredited city polyclinics. The questionnaire comprised 38 items assessing employee satisfaction, teamwork cohesion, and perceived service quality.

Results: A total of 191 healthcare workers participated in the study with a response rate of more than 95%. Overall, 54.4% of respondents reported positive attitudes toward JCI accreditation, and 4.1% expressed very positive views. Perceptions of JCI's impact on patient safety were more favorable, with 63.3% rating it positively. Respondents with more than five years of work experience expressed satisfaction that JCI accreditation enhanced patient safety. In addition, participants with postgraduate education (β = 0.25, 95% CI: 0.53-0.65; p < .001) and younger than 34 years (β = 0.555, 95% CI: 0.35-0.76; p < .001) were likely to recommend JCI accreditation.

Conclusion: Healthcare professionals perceive JCI accreditation as a positive driver of patient safety, teamwork, and institutional quality culture. Implementing JCI measures may improve patient outcomes, strengthen staff engagement, and facilitate alignment with international standards.

Numerous physiological observations have shown that the brain operates at the edge of a critical state between order and disorder. Meanwhile, brain structures at different scales, from cortical columns to the entire brain, are organized in a modular manner. However, whether modular brain networks represent the optimized structure shaped for criticality and in what ways, have not been fully answered. In this study, a modular network with dense intra-module links but sparse inter-module links is established, and the behavior of each neuron is governed by the Kinouchi-Copelli model. Moreover, randomized surrogate networks with identical degree distribution are introduced to illustrate the significance of modular structures for criticality. Results suggest that the modular network requires fewer synaptic resources and lower firing costs to achieve criticality. More importantly, smaller avalanches indicate that the modular structures can enhance network resilience, facilitating rapid recovery from perturbations. Furthermore, by testing the sensitivity of the network state to local excitatory-inhibitory fluctuations, it is found that the efficiency of excitatory and inhibitory regulation is closely related to the 2-level excitatory input density. In addition, inhibitory regulation targeting modules with larger maximum real eigenvalues can more effectively suppress hyperexcitatory activities to achieve balance. When local excitation is greatly enhanced, even if the modular network is adjusted to the critical state, the size-to-duration ratio of module-level avalanches can effectively capture abnormalities. The properties also manifest in clinical recordings from patients with temporal lobe epilepsy, which may provide a promising method for epileptogenic zone localization.

Detecting seizures automatically is crucial for diagnosing and treating epilepsy, substantially benefiting affected patients. Various deep learning models and methods have been developed to automatically extract features from electroencephalogram (EEG) data for detecting seizures, but may often fail to adequately capture the significant periodic and semi-periodic dynamics in EEG signals, thus incompletely representing the extracted features. To address this challenge, we here introduced a novel EEG feature learning framework named ContrLF. This framework combines a contrastive learning framework and the Floss method to improve EEG feature learning for epileptic seizure detection. In our methodology, initially, both strong and weak augmentation are applied to transform the original EEG data into two distinct yet correlated views. Then, Floss is employed to automatically detect and learn the primary periodic dynamics within the augmented EEG data, capturing meaningful periodic representations that are essential for understanding seizure patterns in EEG signals. In parallel, the augmented EEG data were sequentially processed through temporal and contextual contrasting modules, which are designed to learn robust feature representations of the EEG signals. Finally, a Support Vector Machine (SVM) classifier was used to evaluate the effectiveness of the EEG features extracted using our proposed framework. Experimental results generated using both scalp and intracranial electroencephalogram (iEEG) datasets revealed that the proposed framework achieves over 90% accuracy, sensitivity, and specificity in detecting seizures. The framework outperforms other state-of-the-art methods, demonstrating its superiority in both cross-patient and specific-patient seizure detection.

Objective: This review aims to summarize current progress in targeted therapy for acute myeloid leukemia (AML) with KMT2A rearrangement (KMT2Ar). This subtype of AML often shows resistance to chemotherapy and has a poor prognosis. The purpose is to emphasize potential therapeutic strategies and explore drugs currently under clinical development. Methods: We reviewed studies on the molecular characteristics of KMT2Ar AML and examined targeted drugs that can block key genetic and epigenetic mechanisms. Information on drug mechanisms, preclinical findings, and clinical trials was collected and analyzed.

Results: Several new agents targeting KMT2A-related pathways are being explored. Menin inhibitors show encouraging clinical activity, while other inhibitors, such as those targeting DOT1L, BET, and EZH2, have produced promising preclinical results. Early data suggest that combination therapy may be more effective in overcoming drug resistance than monotherapy.

Discussion: Providing a new therapeutic direction for the abnormal molecular networks in KMT2Ar AML offers a promising approach. However, most therapies are still in the early stages and clinical translation is limited. Further research is needed to improve the safety and long-term efficacy of the treatment.

Conclusion: There is an urgent need for effective targeted drugs for KMT2Ar AML. Continuous research and clinical trials will be key to improving patient prognosis and advancing precise treatment for this challenging leukemia subtype.

Iron is an essential cofactor in metabolic and developmental processes. Iron deficiency (ID) is the most common micronutrient deficiency in pregnancy, especially impacting medically underserved populations worldwide. Iron deficiency (ID) in pregnancy predisposes neonates to poor iron status, i.e., congenital ID and associated adverse effects. The role of congenital ID on human kidney development is unstudied, but impaired fetal kidney development is possible. Both vascular and global nutrient restriction rat models report impaired fetal kidney development, as well as induce hypertension, supporting the developmental origins of health and disease (DOHaD) hypothesis. This review compiles findings from 17 published studies in rats examining congenital or early postnatal ID, showing the same. The review compares histological and physiological findings in both congenital and postnatal ID, placing these in the context of recent knowledge describing molecular mechanistic pathways regulating nephrogenesis. Findings in rat early-life ID include lower kidney iron levels, lower glomerular generations and estimated glomerular numbers, larger maculae densa size, interstitial fibrosis, and prolonging active glomerulogenesis past normal temporal cessation. Additionally, several physiological studies in rat congenital ID promote altered renin-angiotensin signaling and hypertension with maturation, especially in males. Key findings of morphological kidney maldevelopment, altered renin-angiotensin signaling, and hypertension in early-life ID underscore the urgent need for future mechanistic data in animals such as rats. The long-term goal would be to leverage understanding from these data into either preventative or early therapeutic strategies in children.

Background: Patients with chronic kidney disease (CKD) stage 5D receiving peritoneal dialysis (PD) are at risk for thiamin deficiency (TD). This study compared the proportion of TD in pediatric CKD patients undergoing PD with that in healthy controls and evaluated the associations of various factors with TD in CKD patients.

Methods: Thirty-two patients with CKD stage 5D and 34 healthy children were recruited. The participants reported their consumption of foods containing antithiamin factors and completed a 3-day food record to assess their intake of thiamin, energy, and macronutrients. The medical records of the CKD group were reviewed. Thiamin status was assessed via an erythrocyte transketolase activity assay, where the thiamin pyrophosphate effect was determined.

Results: Thirteen percent of participants in the CKD group had TD, whereas 29% of the healthy controls did (p = 0.093). The CKD group had significantly greater total thiamin intake per 1,000 kcal of energy due to thiamin supplementation (2.14 [1.83, 2.99] vs. 0.87 [0.59, 1.14] mg/1,000 kcal; p < 0.001), despite inadequate dietary thiamin intake. A longer PD duration (in months) and a high-transport peritoneal membrane status were significantly associated with poorer thiamin status (β = + 0.59, p < 0.001, and β = + 0.38, p = 0.013, respectively). In contrast, greater total thiamin intake was correlated with improved thiamin status (β = -0.35, p = 0.022).

Conclusions: Thiamin deficiency was observed in 13% of pediatric CKD patients on PD and 29% of healthy controls. In CKD patients, TD was associated with longer PD duration (in months), high-transport peritoneal membrane status, and low total thiamin intake.

DDX17 (DEAD-box RNA helicase 17) is an essential RNA helicase and regulatory ATPase in host cells, extensively involved in various cellular processes during viral infections, such as RNA splicing, transcriptional regulation, and post-transcriptional modification. DDX17 exhibits dual functionality in viral infections: it enhances the stability, packaging, and replication of viral RNA through interactions with viral ribonucleoprotein complexes, as evidenced in infections caused by influenza viruses and Hantaan virus (HTNV). Conversely, DDX17 can inhibit viral proliferation by disrupting viral RNA metabolism, as observed in hepatitis B virus (HBV) and Epstein-Barr virus (EBV) infections, where it suppresses replication by modulating viral RNA decapping and degradation. The dual role of DDX17 provides novel insights into host-virus interactions while also highlighting its significant potential as an antiviral therapeutic target. These findings are expected to establish a theoretical foundation for related research and offer valuable references for developing novel antiviral strategies.

We introduce Ibex, a pan-immunoglobulin structure prediction model for antibodies, nanobodies, and T-cell receptors. Unlike previous approaches, Ibex explicitly distinguishes between bound and unbound protein conformations by training on labeled apo and holo structural pairs, enabling accurate prediction of both states at inference time. Ibex achieves state-of-the-art accuracy, demonstrating superior out-of-distribution performance on a comprehensive benchmark of high-resolution antibody structures with a mean CDR H3 RMSD of 2.28 Å. Ibex combines this accuracy with significantly reduced computational requirements, providing a robust foundation for accelerating large molecule design and therapeutic development.