Highly Efficient and Stable Perovskite Solar Cells: Competitive Crystallization Strategy and Synergistic Passivation.

Defects of perovskite (PVK) films are one of the main obstacles to achieving high-performance perovskite solar cells (PSCs). Here, the authors fabricated highly efficient and stable PSCs by introducing prolinamide (ProA) into the PbI2 precursor solution, which improves the performance of PSCs by the competitive crystallization and efficient defect passivation of perovskite. The theoretical and experimental results indicate that ProA forms an adduct with PbI2 , competes with free I- to coordinate with Pb2+ , leads to the increase of the energy barrier of crystallization, and slows down the crystallization rate. Furthermore, the dual-site synergistic passivation of ProA is revealed by density functional theory (DFT) calculations and experimental results. ProA effectively reduces non-radiative recombination in the resultant films to improve the photovoltaic performance of PSCs. Notably, ProA-assisted PSCs achieve 24.61% power conversion efficiency (PCE) for the champion device and the stability of PSCs devices under ambient and thermal environments is improved.

Improving sarcopenia in older adults: a systematic review and meta-analysis of randomized controlled trials of whey protein supplementation with or without resistance training.

OBJECTIVES: The aim of the study was to comprehensively analyze the effects of whey protein (WP)-enriched supplement intake with or without resistance training (RT) in older patients, either from the community or hospital, who were diagnosed with sarcopenia according to the EWGSOP or AWGS criteria. METHODS: This meta-analysis study was registered in PROSPERO (CRD42023407885). We searched the PubMed, Embase, Web of Science, and Cochrane Library databases for RCTs up to June 1, 2023. Standardized mean differences (SMD) with 95% confidence intervals (CI) were used to estimate the pooled results. RESULTS: Ten RCT studies, including 1154 participants, were included and analyzed. The primary outcomes were the changes in muscle mass, strength, and physical performance. In WP group versus (vs.) Isocaloric placebo (PLA)/Routine consultation (RC) group, WP significantly increased the appendicular skeletal muscle mass index (SMD: 0.47, 95%CI: 0.23, 0.71), appendicular skeletal muscle mass (SMD: 0.28, 95%CI: 0.11, 0.45) and gait speed (SMD: 1.13, 95%CI: 0.82, 1.44) in older patients with sarcopenia. In WP with RT group vs. PLA/ RC group, there was significant increase in handgrip strength (SMD: 0.67, 95%CI: 0.29, 1.04). In addition, in the secondary outcomes, WP significantly reduced interleukin-6, significantly increased insulin-like growth factor-1 and albumin, promoted participants' intake of total energy and protein, enhanced activities of daily living scores in patients, and had no significant effect on BMI, weight, or fat mass. CONCLUSION: This review confirms that WP can improve various aspects of older adult with sarcopenia, thereby enhancing their overall physical condition. More studies should be conducted to validate this result and further explore the effects of WP and RT in patients with sarcopenia.

Effect of Pre-Operative Low Serum Pre-Albumin on Surgical Site Infection in Post-Surgery Subjects: A Systematic Review and Meta-Analysis.

Background: The correlation between pre-operative serum pre-albumin and surgical site infection (SSI) has been the focus of many studies. However, existing literature presents conflicting evidence on this association. Therefore, this meta-analysis was conducted to determine the significance of low serum pre-albumin as a prognostic factor SSI, and to assess the potential utility of pre-albumin in predicting SSI. Methods: A comprehensive literature search and analysis was conducted in PubMed, Web of Science, Cochrane of Library, Scopus, Embase, and Google Scholar databases through August 2022 to identify studies reporting low pre-operative serum pre-albumin levels in patients undergoing surgery and their association with SSIs. The pooled risk estimates were shown in odds ratio with 95% confidence interval. The random effect model was used according to the test of heterogeneity among studies. Subgroup analyses and sensitivity analyses were performed to identify the possible sources of heterogeneity. This meta-analysis was prospectively registered in the PROSPERO database (number: CRD42022376167). Results: Nine studies involving 5,306 patients were eligible. The results demonstrated an association between low pre-operative serum pre-albumin levels and a higher probability of developing SSI (odds ratio [OR], 2.04; 95% confidence interval [CI], 1.28-3.26). Conclusions: Our findings suggest that low serum pre-albumin level may serve as an independent and valuable predictor of SSI. These results provide important insights for clinicians in identifying high-risk patients and implementing preventive measures.

Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis.

The relationship between PLIN2 expression and prognosis, and clinicopathological significance of various cancers has been extensively studied, but the results are not completely consistent. This review followed the guidelines for systematic reviews of prognostic factors studies and was reported under the Preferred Reporting Program for Systematic Reviews and Meta-Analysis (PRISMA). We searched PubMed, Embase, Cochrane Library, Web of Science, and Google Academia for relevant articles up to September 2, 2022, and calculated the pooled hazard ratios (HR) with 95% confidence intervals (CI) to determine the association between PLIN2 expression and the prognosis of various cancers. The meta-analysis ultimately included 17 studies. The quality of all included cohort studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool, and an adaptation of Grading of Recommendations Assessment, Development and Evaluation (GRADE) method was used to assess the certainty of the results. High expression of PLIN2 was associated with poorer overall survival (HR = 1.65; 95% CI = 1.14, 2.38; P = 0.008), metastasis-free survival (HR = 1.48; 95% CI = 1.12, 1.94; P = 0.005), progression-free survival (HR = 2.11; 95% CI = 1.55, 2.87; P < 0.0005) and recurrence-free survival/relapse-free survival (HR = 2.21; 95% CI = 1.64, 2.98; P < 0.0005) in cancers. The clinicopathological parameters of digestive system malignancies suggested that high expression of PLIN2 was notably associated with distant metastasis ( + ) (odds ratio (OR) = 3.37; 95% CI = 1.31, 8.67; P = 0.012), lymph node metastasis ( + ) (OR = 1.61; 95% CI = 1.01, 2.54; P = 0.004), and tumor stage (III-IV) (OR = 1.96; 95% CI = 1.24, 3.09; P = 0.006). In summary, overexpression of PLIN2 is significantly associated with a poor prognosis in various human cancers, especially in respiratory and digestive malignancies. Thus, PLIN2 expression may be a potential prognostic biomarker in cancer patients.

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis.

Primary headache is a very common and burdensome functional headache worldwide, which can be classified as migraine, tension-type headache (TTH), trigeminal autonomic cephalalgia (TAC), and other primary headaches. Managing and treating these different categories require distinct approaches, and accurate diagnosis is crucial. Functional magnetic resonance imaging (fMRI) has become a research hotspot to explore primary headache. By examining the interrelationships between activated brain regions and improving temporal and spatial resolution, fMRI can distinguish between primary headaches and their subtypes. Currently the most commonly used is the cortical brain mapping technique, which is based on blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI). This review sheds light on the state-of-the-art advancements in data analysis based on fMRI technology for primary headaches along with their subtypes. It encompasses not only the conventional analysis methodologies employed to unravel pathophysiological mechanisms, but also deep-learning approaches that integrate these techniques with advanced statistical modeling and machine learning. The aim is to highlight cutting-edge fMRI technologies and provide new insights into the diagnosis of primary headaches.