ObjectiveChronic atrophic gastritis (CAG) is usually diagnosed by gastroscopy and histopathological biopsy. These procedures remain the reference standard, but their invasive nature and resource requirements may limit their use in large-scale population screening and repeated follow-up. A convenient and reproducible method for noninvasive auxiliary screening may help identify individuals who require further endoscopic assessment. Fingertip photoplethysmography (PPG) provides a noninvasive recording of peripheral pulse waves and allows harmonic features to be extracted from the signal. In this study, the so-called meridian-related variables were defined as PPG-derived harmonic parameters labelled according to meridian nomenclature, rather than as direct measurements of meridian physiology. This study aimed to compare these harmonic parameters between patients with CAG and non-CAG controls, identify parameters that remained different after age adjustment, and develop a multivariable model for noninvasive auxiliary screening and pre-endoscopic risk stratification of CAG. MethodsA total of 343 participants were included, comprising 171 patients with CAG and 172 non-CAG controls. CAG diagnosis was established using gastroscopy and histopathology as the reference standard. Fingertip PPG signals were collected using a PPG-based pulse acquisition device. Eight PPG-derived harmonic parameters labelled according to meridian nomenclature were extracted for analysis. Between-group differences were first assessed using nonparametric tests. Age-adjusted analyses were then performed to reduce potential confounding by age. The false discovery rate (FDR) method was applied for multiple-comparison correction. A multivariable logistic regression model integrating age and multiple harmonic parameters was constructed. Model performance was evaluated using receiver operating characteristic (ROC) analysis and the area under the curve (AUC). Internal validation performance was assessed using stratified five-fold cross-validation and bootstrap optimism correction. Threshold performance was examined using both a high-specificity strategy and a Youden index-based cutoff. Decision curve analysis was used to evaluate the model’s net clinical benefit across a range of threshold probabilities. ResultsAll eight harmonic parameters were non-normally distributed. In the univariate analysis, the stomach-labelled harmonic parameter (ST), bladder-labelled harmonic parameter (BL), and liver-labelled harmonic parameter (LR) differed between the CAG and non-CAG groups. After age adjustment and FDR correction, only ST and BL remained statistically significant. Compared with non-CAG controls, patients with CAG showed higher ST values and lower BL values. This finding indicates an associated differential harmonic pattern that was not fully explained by age distribution. However, the discriminative ability of a single harmonic parameter was limited. The best-performing single indicator was ST, with an AUC of 0.652 (95% CI: 0.595-0.707). The multivariable model integrating age and multiple harmonic parameters achieved an AUC of 0.791 (95% CI: 0.743-0.835), representing an improvement of 0.139 over ST alone. In internal validation, stratified five-fold cross-validation yielded a mean AUC of 0.753 (95% CI: 0.715-0.781), and the bootstrap optimism-corrected AUC was 0.748. These results suggest that the model retained moderate discriminative performance after internal validation.At a specificity of at least 95%, the model achieved a sensitivity of only 40.4% (95% CI: 25.7%-49.7%). This high-specificity cutoff may be suboptimal as the preferred threshold for an initial screening setting because of the potential risk of missed CAG cases. The Youden index-based optimal cutoff was 0.419, corresponding to a sensitivity of 80.7% and a specificity of 62.8%. This threshold may better match the practical aim of noninvasive auxiliary screening, where sensitivity is usually prioritized to reduce missed cases. Decision curve analysis showed that, within a threshold probability range of 10%-55%, the model provided higher net clinical benefit than the reference strategies of recommending gastroscopy for all participants or for none. ConclusionPatients with CAG showed associated harmonic differences in fingertip PPG-derived features, mainly characterized by higher ST and lower BL values after age adjustment and FDR correction. Compared with a single harmonic parameter, the multivariable model showed better overall discrimination and retained moderate internal validation performance. These findings suggest that PPG-derived harmonic parameters labelled according to meridian nomenclature may provide auxiliary information for noninvasive auxiliary screening and front-line triage before gastroscopic confirmation in CAG. The present results support further validation rather than immediate clinical implementation. External validation in independent, multicenter, and preferably prospective screening cohorts is needed to assess the model’s generalizability, screening performance, and potential clinical utility.

ObjectiveChronic atrophic gastritis (CAG) is usually diagnosed by gastroscopy and histopathological biopsy. These procedures remain the reference standard, but their invasive nature and resource requirements may limit their use in large-scale population screening and repeated follow-up. A convenient and reproducible method for noninvasive auxiliary screening may help identify individuals who require further endoscopic assessment. Fingertip photoplethysmography (PPG) provides a noninvasive recording of peripheral pulse waves and allows harmonic features to be extracted from the signal. In this study, the so-called meridian-related variables were defined as PPG-derived harmonic parameters labelled according to meridian nomenclature, rather than as direct measurements of meridian physiology. This study aimed to compare these harmonic parameters between patients with CAG and non-CAG controls, identify parameters that remained different after age adjustment, and develop a multivariable model for noninvasive auxiliary screening and pre-endoscopic risk stratification of CAG. MethodsA total of 343 participants were included, comprising 171 patients with CAG and 172 non-CAG controls. CAG diagnosis was established using gastroscopy and histopathology as the reference standard. Fingertip PPG signals were collected using a PPG-based pulse acquisition device. Eight PPG-derived harmonic parameters labelled according to meridian nomenclature were extracted for analysis. Between-group differences were first assessed using nonparametric tests. Age-adjusted analyses were then performed to reduce potential confounding by age. The false discovery rate (FDR) method was applied for multiple-comparison correction. A multivariable logistic regression model integrating age and multiple harmonic parameters was constructed. Model performance was evaluated using receiver operating characteristic (ROC) analysis and the area under the curve (AUC). Internal validation performance was assessed using stratified five-fold cross-validation and bootstrap optimism correction. Threshold performance was examined using both a high-specificity strategy and a Youden index-based cutoff. Decision curve analysis was used to evaluate the model’s net clinical benefit across a range of threshold probabilities. ResultsAll eight harmonic parameters were non-normally distributed. In the univariate analysis, the stomach-labelled harmonic parameter (ST), bladder-labelled harmonic parameter (BL), and liver-labelled harmonic parameter (LR) differed between the CAG and non-CAG groups. After age adjustment and FDR correction, only ST and BL remained statistically significant. Compared with non-CAG controls, patients with CAG showed higher ST values and lower BL values. This finding indicates an associated differential harmonic pattern that was not fully explained by age distribution. However, the discriminative ability of a single harmonic parameter was limited. The best-performing single indicator was ST, with an AUC of 0.652 (95% CI: 0.595-0.707). The multivariable model integrating age and multiple harmonic parameters achieved an AUC of 0.791 (95% CI: 0.743-0.835), representing an improvement of 0.139 over ST alone. In internal validation, stratified five-fold cross-validation yielded a mean AUC of 0.753 (95% CI: 0.715-0.781), and the bootstrap optimism-corrected AUC was 0.748. These results suggest that the model retained moderate discriminative performance after internal validation.At a specificity of at least 95%, the model achieved a sensitivity of only 40.4% (95% CI: 25.7%-49.7%). This high-specificity cutoff may be suboptimal as the preferred threshold for an initial screening setting because of the potential risk of missed CAG cases. The Youden index-based optimal cutoff was 0.419, corresponding to a sensitivity of 80.7% and a specificity of 62.8%. This threshold may better match the practical aim of noninvasive auxiliary screening, where sensitivity is usually prioritized to reduce missed cases. Decision curve analysis showed that, within a threshold probability range of 10%-55%, the model provided higher net clinical benefit than the reference strategies of recommending gastroscopy for all participants or for none. ConclusionPatients with CAG showed associated harmonic differences in fingertip PPG-derived features, mainly characterized by higher ST and lower BL values after age adjustment and FDR correction. Compared with a single harmonic parameter, the multivariable model showed better overall discrimination and retained moderate internal validation performance. These findings suggest that PPG-derived harmonic parameters labelled according to meridian nomenclature may provide auxiliary information for noninvasive auxiliary screening and front-line triage before gastroscopic confirmation in CAG. The present results support further validation rather than immediate clinical implementation. External validation in independent, multicenter, and preferably prospective screening cohorts is needed to assess the model’s generalizability, screening performance, and potential clinical utility.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

Oral solid dosage forms require processes such as disintegration and dissolution to release the drug before it can be absorbed and utilized by the body. In this manuscript, imaging technology was used to continuously visualize and characterize the in vitro static drug release process of gliclazide modified release tablets from 15 manufacturers, combined with the traditional method of in vitro dissolution testing, to determine the release profile of gliclazide modified release tablets, to evaluate the similarity of the release profiles by using the similarity factor (f₂) method and based on the analysis of the release profiles fitted with a variety of mathematical models. The results indicate that the gliclazide modified release tablets produced by 14 companies are hydrophilic gel matrix tablets. Compared to the reference listed drug, the release profiles of formulations from 11 companies show high similarity (f₂ > 50) to the reference. Among these, formulations with visual characteristics similar to the reference exhibit similar release curves. This study provides an alternative method for the in vitro consistency evaluation of gliclazide modified release tablets, aiming to assess the in vitro release behaviour of generic formulations more accurately and comprehensively.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Brain’s neural activities encompass both periodic rhythmic oscillations and aperiodic neural fluctuations. Rhythmic oscillations manifest as spectral peaks of neural signals, directly reflecting the synchronized activities of neural populations and closely tied to cognitive and behavioral states. In contrast, aperiodic fluctuations exhibit a power-law decaying spectral trend, revealing the multiscale dynamics of brain neural activity. In recent years, researchers have made notable progress in studying brain aperiodic dynamics. These studies demonstrate that aperiodic activity holds significant physiological relevance, correlating with various physiological states such as external stimuli, drug induction, sleep states, and aging. Aperiodic activity serves as a reflection of the brain’s sensory capacity, consciousness level, and cognitive ability. In clinical research, the aperiodic exponent has emerged as a significant potential biomarker, capable of reflecting the progression and trends of brain diseases while being intricately intertwined with the excitation-inhibition balance of neural system. The physiological mechanisms underlying aperiodic dynamics span multiple neural scales, with activities at the levels of individual neurons, neuronal ensembles, and neural networks collectively influencing the frequency, oscillatory patterns, and spatiotemporal characteristics of aperiodic signals. Aperiodic dynamics currently boasts broad application prospects. It not only provides a novel perspective for investigating brain neural dynamics but also holds immense potential as a neural marker in neuromodulation or brain-computer interface technologies. This paper summarizes methods for extracting characteristic parameters of aperiodic activity, analyzes its physiological relevance and potential as a biomarker in brain diseases, summarizes its physiological mechanisms, and based on these findings, elaborates on the research prospects of aperiodic dynamics.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.