Objective: To analyze the mortality trend of chronic respiratory diseases in Lianyungang City, Jiangsu Province from 2011 to 2024, and predict the mortality from 2025 to 2029, so as to provide the evidence for developing forward-looking prevention and control strategies and targeted intervention measures. Methods: Chronic respiratory disease death surveillance data in Lianyungang City from 2011 to 2024 were collected through the Jiangsu Chronic Disease Management Information Platform. The crude mortality was calculated and standardized using the data of the Sixth National Population Census in 2010. Average annual percent change (AAPC) was used to analyze the mortality trends of chronic respiratory diseases by gender, region, and age in Lianyungang City from 2011 to 2024. The GM (1,1) grey model was established using the crude mortality of chronic respiratory diseases from 2011 to 2024 to predict the crude mortality of chronic respiratory diseases from 2025 to 2029. Results: From 2011 to 2024, the crude mortality and standardized mortality of chronic respiratory diseases in Lianyungang City were 52.20/105 and 36.57/105, respectively. The crude mortality and standardized mortality were 53.14/105 and 43.79/105 in males, respectively, which were higher than 51.17/105 and 30.14/105 in females (both P<0.05). The crude mortality and standardized mortality were 55.62/105 and 39.85/105 in rural areas, respectively, which were higher than 38.11/105 and 24.15/105 in urban areas (both P<0.05). From 2011 to 2024, the standardized mortality of chronic respiratory diseases showed downward trends in the total population, males, females, urban areas, and rural areas (AAPC=-6.956%, -5.175%, -8.659%, -4.290%, and -7.369%, all P<0.05). The crude mortality of chronic respiratory diseases in the total population showed an upward trend with increasing age (P<0.05), reaching a peak of 1 371.20/105 in the ≥80 years group. The top two causes of chronic respiratory disease deaths were chronic obstructive pulmonary disease and bronchial asthma, with 31 792 cases and 3 254 cases, accounting for 82.40% and 8.43%, respectively. The GM (1,1) grey model prediction results showed that the crude mortality of chronic respiratory diseases in the total population from 2025 to 2029 would be 37.91/105, 36.32/105, 34.80/105, 33.34/105, and 31.95/105, respectively (average relative error=8.76%). Conclusions From 2011 to 2024, the mortality of chronic respiratory diseases in Lianyungang City showed a downward trend. The risk of death from chronic respiratory diseases was higher in males, rural areas, and the elderly. It is predicted that the mortality of chronic respiratory diseases will decrease year by year from 2025 to 2029.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Objective:To evaluate the utility of pulse oximetry-derived parameters—specifically, the pulse oximetry plethysmographic waveform area under the curve (POP AUC) and the peripheral perfusion index (PPI)—in assessing disease severity and predicting prognosis in patients with sepsis. Methods:In this prospective descriptive study, 68 patients with sepsis were categorized based on illness severity into septic shock and non-shock groups, and by 28-day outcome into survival and non-survival groups. POP AUC, PPI, and lactate (Lac) levels were recorded at 0, 24, 48, 72, and 96 hours after admission. APACHEⅡ and SOFA scores were calculated within the first 24 hours. The prognostic value of these parameters was evaluated. Results:Significant differences were observed between the septic shock and non-shock groups in POP AUC, PPI, Lac (all P < 0.05 except at 96 h), APACHEⅡ, and SOFA scores (all P < 0.05). These differences were most pronounced at admission: POP AUC0 (2475.1 ± 899.0) vs. (4260.3 ± 1028.5), PPI 0 (0.78 ± 0.74) vs. (3.13 ± 2.18), Lac 0 (4.95 ± 4.32) vs. (2.07 ± 1.55), APACHE Ⅱ (16.78 ± 5.59) vs. 11.82 ± 4.89), and SOFA (8.89 ± 3.25) vs. (5.06 ± 2.60). Optimal prognostic cut-off values were 2741.43 for POP AUC, 0.97 for PPI, 2.05 for Lac, 12.5 for APACHEⅡ, and 5.5 for SOFA. ROC curve analysis showed that at 24 hours, POP AUC and PPI had significantly larger AUC values than Lac ( P < 0.05), while no significant differences were found among other parameters. Significant differences between non-survivors and survivors were also found in POP AUC, PPI (at 0, 24, and 48 h), APACHE II, and SOFA (all P < 0.05). No significant differences were observed in PPI (72 h and 96 h) or Lac between the two outcome groups. Conclusions:POP AUC and PPI, as derived from pulse oximetry, are non-inferior to Lac, SOFA, and APACHEⅡ scores in evaluating disease severity and predicting 28-day mortality in sepsis patients. These parameters show promise as practical and non-invasive tools for clinical assessment in sepsis.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

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Objective:To explore the short-term clinical effects of deep cervical lymph node-venous anastomosis in the treatment of Alzheimer’s disease (AD).Methods:A prospective exploratory study was conducted on the treatment of AD patients using the cervical deep lymph node-venous anastomosis technique in Scar and Wound Treatment Department, Plastic Surgery Hospital, Chinese Academy of Medical Sciences, from September to October 2024. The patients underwent high-frequency ultrasound to locate deep cervical lymph nodes and the external jugular vein. Under general anesthesia, bilateral deep cervical lymph node-venous anastomoses were performed. Indocyanine green (ICG) lymphography was conducted via subcutaneous injection behind the ear to visualize lymph nodes in levels Ⅱ and Ⅲ. After making a skin incision along the posterior margin of the sternocleidomastoid muscle, the external jugular vein, internal jugular veins, and associated lymph nodes were exposed. Adjacent veins were selected for anastomosis of lymph node. Using microsurgical techniques, end-to-side or end-to-end anastomosis was completed for lymph nodes in levels Ⅱ and Ⅲ. Preoperative assessments included the mini-mental state examination (MMSE, a higher score indicates better cognitive function), Alzheimer’s disease assessment scale-cognitive subscale (ADAS-Cog, a higher score indicates greater impairment of cognitive function), Alzheimer’s disease cooperative study scale for activities of daily living (ADCS-ADL, a higher score indicates better ability to perform daily activity), and neuropsychiatric inventory (NPI, a higher score indicates more severe behavioral and emotional symptom). Postoperative follow-up included the same scales to observe changes in cognitive function, activities of daily living, and emotional communication.Results:Four patients (1 male, 3 females, aged 58-79 years) with AD were included. All were diagnosed based on cerebrospinal fluid biomarkers. All patients successfully underwent bilateral deep cervical lymph node-venous anastomoses. On average, 4.3 (2-7 per person) anastomoses were performed per patient. Surgical procedures lasted an average of 6.5 h (5.5-8.5 h) with minimal blood loss (less than 50 ml). Patients resumed normal activity within 6 hours postoperatively and were discharged after an average of 4.1 d (3.5-5.0 d). Postoperative complications included one case each of aspiration pneumonia, lower limb venous thrombosis, and transient delirium, all of whom resolved without long-term effects. Clinical symptoms, including memory decline, mood swings, and anxiety, showed varying degrees of improvement. Patients reported enhanced quality of life, emotional stability, and social engagement, confirming the procedure’s safety and potential cognitive benefits. At one month postoperatively, the MMSE scores of the four patients increased by an average of 0.8 points compared to preoperative levels. Additionally, the two patients who completed the ADAS-Cog assessments showed a decrease in their scores (reduced by 1.0 points and 11.3 points, respectively, compared to preoperative scores), indicating a certain degree of improvement in cognitive function during this period. The ADCS-ADL and NPI scores of four patients varied significantly, without showing any clear pattern.Conclusion:Lymphovenous anastomosis of the deep cervical lymph node-venous anastomosis may provide a new surgical intervention approach for AD, but further large-scale studies and long-term follow-up are needed to validate its safety and effectiveness.

Head and neck tumors are mainly classified into lesions originating from the oral cavity, pharynx, larynx, paranasal sinuses, and salivary glands. Squamous cell carcinoma remains the most common histopathological subtype. The in-depth exploration of genetics and epigenetics can contribute to elucidating the molecular mechanisms of development, progression and metastasis in head and neck squamous cell carcinoma (HNSCC), screening prognostic related biomarkers, which provide new directions and opportunities for targeted therapy. This paper summarizes the primary genetic and epigenetic characteristics of HNSCC and discusses the correlation of key molecules with the prognosis of patients.

Objective To predict the radiation impact of discharging wastewater containing uranium within the specified limit generated during the normal operation of a new production line at a nuclear fuel plant on the receiving water body and its downstream, and to provide a reference for the management of radioactive liquid effluent discharge from nuclear facilities. Methods Based on the technical guidelines for environmental impact assessment, literature on radiation environmental impact assessment, and data collected from on-site investigations, appropriate hydrological parameters and prediction models were selected to analyze and predict the variation pattern of radioactive nuclide uranium along the receiving water body and the radiation exposure of nearby residents. Results The maximum increase in uranium concentration in the receiving water body and its downstream caused by the discharge of uranium-containing wastewater was 1.14 μg/L. The maximum predicted concentration was 2.75 μg/L after adding the background data of the water body. The resulting maximum individual annual effective dose for the public was 1.49 × 10⁻⁴ mSv/a. Conclusion The maximum predicted uranium concentration in the receiving water body and its downstream is lower than the uranium concentration limit of 30 μg/L specified in the Standards for Drinking Water Quality (GB 5749-2022). The maximum individual annual effective dose for the public is much lower than the control value of 0.2 mSv/a specified in the Radiation Protection Regulations for Uranium Processing and Fuel and Fuel Manufacturing Facilities (EJ 1056-2018). The radiation impact is acceptable.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.