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Gastric cancer (GC) is one of the most common tumors worldwide and the leading cause of tumor-related mortality. Endoscopy and serological tumor marker testing are currently the main methods of GC screening, and treatment relies on surgical resection or chemotherapy. However, traditional examination and treatment methods are more harmful to patients and less sensitive and accurate. A minimally invasive method to respond to GC early screening, prognosis monitoring, treatment efficacy, and drug resistance situations is urgently needed. As a result, liquid biopsy techniques have received much attention in the clinical application of GC. The non-invasive liquid biopsy technique requires fewer samples, is reproducible, and can guide individualized patient treatment by monitoring patients' molecular-level changes in real-time. In this review, we introduced the clinical applications of circulating tumor cells, circulating free DNA, circulating tumor DNA, non-coding RNAs, exosomes, and proteins, which are the primary markers in liquid biopsy technology in GC. We also discuss the current limitations and future trends of liquid biopsy technology as applied to early clinical biopsy technology.

Harvesting the energy from the interaction between hygroscopic materials and atmospheric water can generate green and clean energy. However, the ion diffusion process of moisture-induced dissociation leads to the disappearance of the ion concentration gradient gradually, and there is still a lack of moisture-based power generation devices with truly continuous operation, especially the duration of the current output still needs to be extended. Here, we propose a design for reconstructing the ion concentration gradient by coupling photocatalytic hydrogen evolution reaction with hydrovoltaic effect, to report a moisture-enabled electric generator (MEG) with continuous current output. We show that the introduction of the photocatalytic layer not only absorbs light energy to greatly increase the power generation of the MEG (500% power density enhancement), but more importantly, the photocatalytic hydrogen evolution process consumes the pre-stacked ions to restore the ion concentration gradient, allowing the MEG to continuously output current for more than 600 hours, which is 1 to 2 orders of magnitude higher than the great majority of existed MEGs in terms of the current output duration. This study designs a moisture-enabled electric generator (MEG) with a photocatalytic layer, achieving a 500% power density enhancement and continuous current output for over 600 hours, addressing the challenge of extended operation duration

The hybridization chain reaction (HCR) is widely used for biosensing. However, HCR does not provide the required sensitivity. In this study, we reported a method to improve the sensitivity of HCR by dampening the cascade amplification. First, we designed a biosensor based on HCR, and an initiator DNA was used to trigger the cascade amplification. Optimization of the reaction was then performed, and the results showed that the limit of detection (LOD) for the initiator DNA was about 2.5 nM. Second, we designed a series of inhibitory DNAs to dampen the HCR cascade amplification, and DNA dampeners (50 nM) were applied in the presence of the DNA initiator (50 nM). One of the DNA dampeners (D5) showed the best inhibitory efficiency of greater than 80%. This was further applied at concentrations ranging from 0 nM to 10 nM to prohibit the HCR amplification caused by a 2.5 nM initiator DNA (the limit of detection for this initiator DNA). The results showed that 0.156 nM of D5 could significantly inhibit the signal amplification (p<0.05). Additionally, the limit of detection for the dampener D5 was 16 times lower than that for the initiator DNA. Based on this detection method, we achieved a detection limit as low as 0.625 nM for HCV-RNAs. In summary, we developed a novel method with improved sensitivity to detect the target designed to prohibit the HCR cascade. Overall, this method could be used to qualitatively detect the presence of single-stranded DNA/RNA.

Female reproductive system diseases, including malignant tumors such as breast, ovarian, and uterine cancers, and non-neoplastic disorders such as uterine fibroids, endometriosis, polycystic ovary syndrome (PCOS), infertility, and premenstrual syndrome (PMS), represent major health challenges in Asia. While malignant diseases receive considerable attention due to high mortality, the long-term burden of non-malignant conditions affecting fertility, psychological well-being, and quality of life remains underestimated. Data from the Global Burden of Disease (GBD) 2021 were used to quantify the burden of female reproductive system diseases in 47 Asian countries and territories from 1990 to 2021. Incidence, prevalence, mortality, and disability-adjusted life years (DALYs) were analyzed, together with age-standardized rates. Temporal trends were assessed using estimated annual percentage change (EAPC), and disease burden patterns were examined across Socio-demographic Index (SDI) levels. Associations between major modifiable risk factors and disease burden were described. In 2021, female reproductive system diseases accounted for over 33 million DALYs and approximately 600,000 deaths in Asia. Breast and cervical cancers were the leading contributors to mortality and DALYs, whereas PMS and infertility accounted for a substantial burden among younger women. From 1990 to 2021, absolute case numbers and deaths increased with population growth and aging, while most age-standardized rates declined, particularly for cervical cancer. In contrast, PCOS and infertility showed the fastest increases. High body mass index and elevated fasting glucose were strongly associated with breast and uterine cancer burden, while smoking was closely associated with cervical cancer burden. Female reproductive system diseases impose a substantial and heterogeneous burden across Asia. Although malignant cancers remain the main contributors to mortality and DALYs, non-malignant disorders increasingly affect reproductive health, particularly among younger women. Prevention strategies tailored to SDI levels and major risk factors are needed to reduce the overall disease burden across the region.

This study utilized data from the Global Burden of Disease (GBD) to systematically analyze the years lived with disability (YLDs) and direct economic costs attributable to rheumatoid arthritis (RA), osteoarthritis (OA), neck pain (NP), and low back pain (LBP) among women aged 15-49 between 1990 and 2021, aiming to inform public health intervention strategies. Using GBD 2021 data, we reported the YLDs and age-standardized rates (ASYR) for four major musculoskeletal diseases (MSD) in women of childbearing age (WCA) from 1990 to 2021. The temporal trends in disease burden were assessed by estimated annual percentage change (EAPC), and a non-linear frontier model was used to examine the association between the sociodemographic index (SDI) and disease burden. Additionally, the direct economic burden imposed by the four diseases in WCA was estimated. In 2021, the YLDs among WCA worldwide due to RA, OA, NP, and LBP were 610,000, 1.61 million, 6.35 million, and 19.51 million, respectively, reflecting increases of 86.8%, 111%, 58%, and 40% compared to 1990. The ASYR for RA and OA increased by 13.6% and 11.2%, respectively, while the ASYR for NP and LBP slightly decreased. High SDI regions bear the heaviest burden, but regions with low-middle SDI show a faster growth rate. The global economic burden of these four diseases totaled $43.66 billion, with LBP accounting for the largest portion ($16.7 billion), and about 80% of the expenditure concentrated in high-SDI and high-middle SDI countries. MSD pose a persistent and growing health and economic challenge for WCA, with RA and OA showing a clear trend toward younger age groups. Although the ASYR for LBP has decreased, its absolute disability burden has still increased due to population growth. High-income countries bear the majority of the economic cost, reflecting the unequal distribution of global health resources.

Considerable efforts have recently been made to augment the power density of moisture‐enabled electric generators. However, due to the unsustainable ion/water molecule concentration gradients, the ion‐directed transport gradually diminishes, which largely affects the operating lifetime and energy efficiency of generators. This work introduces an electrode chemistry regulation strategy into the ionic diode‐type energy conversion structure, which demonstrates 1240 h power generation in ambient humidity. The electrode chemical regulation can be achieved by adding Cl−. The purpose is to destroy the passivation film on the electrode interface and provide a continuous path for ion‐electron coupling conduction. Moreover, this device simultaneously satisfies the requirements of fast trapping of moisture molecules, high rectification ratio transport of ions, and sustained ion‐to‐electron current conversion. A single device can deliver an open‐circuit voltage of about 1 V and a peak short‐circuit current density of 350 µA cm−2. Finally, the first‐principle calculations are carried out to reveal the mechanism by which the electrode surface chemistry affects the power generation performance. A moisture‐enabled electric generator for power generation at high humidity for 1240 h is presented. The device simultaneously meets the requirements of fast capture of moisture molecules, high rectification ratio transport of ions, and sustained ion‐electron current conversion, and also provides a reliable solution for ultra‐long‐time humidity power generation.

The overuse of antibiotics and the continuous evolution of drug-resistant bacteria present substantial obstacles to the clinical treatment of Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections. This study reports the rational design of a bifunctional fusion polypeptide, S-thanatin (Ts)-functionalized elastin-like carrier (Ts-ELP), which integrates the antimicrobial peptide (AMP) Ts with a human-derived elastin‑like polypeptides (ELPs). This designer polypeptide serves as a smart carrier, capable of efficiently encapsulating the broad-spectrum antibiotic tigecycline (Tig) via reversible temperature-responsive phase transition hydrophobic interaction, resulting in a self-assembled, targeted nano-antibiotic, Tig@Ts-ELP (Tig@TsE). Comprehensive in vitro and in vivo evaluations demonstrate that Tig@TsE exhibits multiple advantageous features, including rapid and simple preparation, specific lipopolysaccharide (LPS)-targeting capability, excellent in vivo targeted delivery efficiency, and dual-responsive drug release triggered by the acidic pH and elevated matrix metalloproteinase-9 (MMP-9) levels in the CRKP-infected microenvironment, coupled with favorable biocompatibility. Moreover, the Ts peptide and Tig exhibit marked synergistic antibacterial effects against CRKP. This innovative nano-antibiotic delivery system is designed to increase local drug concentration at the infection site, boost bactericidal activity, and reduce systemic toxicity, thereby presenting a promising therapeutic strategy for the treatment of drug-resistant bacterial pneumonia. Graphical Abstract

Bone is responsible for providing mechanical protection, attachment sites for muscles, hematopoiesis micssroenvironment, and maintaining balance between calcium and phosphorate. As a highly active and dynamically regulated organ, the balance between formation and resorption of bone is crucial in bone development, damaged bone repair, and mineral homeostasis, while dysregulation in bone remodeling impairs bone structure and strength, leading to deficiency in bone function and skeletal disorder, such as osteoporosis. Osteoporosis refers to compromised bone mass and higher susceptibility of fracture, resulting from several risk factors deteriorating the balanced system between osteoblast‐mediated bone formation and osteoclast‐mediated bone resorption. This balanced system is strictly regulated by translational modification, such as phosphorylation, methylation, acetylation, ubiquitination, sumoylation, glycosylation, ADP‐ribosylation, S‐palmitoylation, citrullination, and so on. This review specifically describes the updating researches concerning bone formation and bone resorption mediated by posttranslational modification. We highlight dysregulated posttranslational modification in osteoblast and osteoclast differentiation. We also emphasize involvement of posttranslational modification in osteoporosis development, so as to elucidate the underlying molecular basis of osteoporosis. Then, we point out translational potential of PTMs as therapeutic targets. This review will deepen our understanding between posttranslational modification and osteoporosis, and identify novel targets for clinical treatment and identify future directions. This review summarizes the updating researches concerning bone formation and bone resorption mediated by different types of posttranslational modification. We highlight dysregulated posttranslational modification in osteoblast and osteoclast differentiation. We then emphasize involvement of posttranslational modification in osteoporosis development, so as to elucidate the underlying molecular basis of osteoporosis. We also suggest translational potential of PTMs as therapeutic targets .

Early and accessible detection of Alzheimer’s disease (AD) remains a major clinical challenge. We developed a machine learning–based blood transcriptomic model, the Lactylation-Derived Score (LDS), from lactylation-related genes across nine AD cohorts, using a standardized pipeline with z-score normalization, random forest–based feature screening, plsRglm modeling, and 10-fold cross-validation. LDS was externally tested in seven independent brain transcriptomic datasets and clinically validated in an independent plasma cohort ( n  = 540); logistic regression was used to integrate LDS with plasma phosphorylated tau 181 (p-tau181) and p-tau217. LDS achieved an AUC of 0.897 (95% CI 0.849–0.934) in the Training Cohort and 0.772 (95% CI 0.729–0.815) in the plasma validation cohort, while the three-marker model (LDS + p-tau181 + p-tau217) yielded the highest diagnostic performance (AUC 0.859, 95% CI 0.824–0.893). LDS alone effectively identified AT⁺ individuals (AUC 0.861, 95% CI 0.827–0.897), and a five-gene classifier derived from LDS genes stratified amnestic mild cognitive impairment with an AUC of 0.809 (95% CI 0.714–0.836). LDS-high individuals showed neuroinflammatory activation and metabolic stress signatures, indicating that this scalable, interpretable transcriptomic model complements plasma p-tau biomarkers and supports precision digital medicine in AD.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed.Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression.Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts.Conclusion: Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.