Iron is an essential trace element in the human body, crucial in maintaining normal physiological functions. Recent studies have identified iron ions as a significant factor in initiating the ferroptosis process, a novel mode of programmed cell death characterized by iron overload and lipid peroxide accumulation. The iron metabolism pathway is one of the primary mechanisms regulating ferroptosis, as it maintains iron homeostasis within the cell. Numerous studies have demonstrated that abnormalities in iron metabolism can trigger the Fenton reaction, exacerbating oxidative stress, and leading to cell membrane rupture, cellular dysfunction, and damage to tissue structures. Therefore, regulation of iron metabolism represents a key strategy for ameliorating ferroptosis and offers new insights for treating diseases associated with iron metabolism imbalances. This review first summarizes the mechanisms that regulate iron metabolic pathways in ferroptosis and discusses the connections between the pathogenesis of various diseases and iron metabolism. Next, we introduce natural and synthetic small molecule compounds, hormones, proteins, and new nanomaterials that can affect iron metabolism. Finally, we provide an overview of the challenges faced by iron regulators in clinical translation and a summary and outlook on iron metabolism in ferroptosis, aiming to pave the way for future exploration and optimization of iron metabolism regulation strategies.

With the widespread adoption of lung cancer screening, an increasing number of patients are being diagnosed with early-stage lung adenocarcinoma. For stage ⅠA lung adenocarcinoma, sublobar resection is the primary treatment approach. However, in patients with concomitant spread through air space (STAS), numerous studies advocate for lobectomy as the mainstay of treatment. Due to the limitations in preoperative prediction and intraoperative frozen section evaluation for assessing STAS, current research is largely restricted to using clinical and imaging features to predict STAS occurrence, with results that are inconsistent and unsatisfactory. Furthermore, most studies focus on individual clinical or imaging characteristics, and there is a lack of large-sample investigations. The rise of artificial intelligence in recent years has provided new insights into solving this problem, and existing studies have shown that artificial intelligence demonstrates better performance in STAS prediction compared to conventional methods. This article reviews the value of artificial intelligence in predicting STAS.

Objective To study the effect of Ditan Decoction combined with aripiprazole and olanzapine in the treatment of schizophrenia and its influence on serum inflammatory factors chnage.Methods Seventy-seven patients with schizophrenia meeting the requirements visiting the outpatient department and hospitalized in Dazu District Hospital of Traditional Chinese Medicine and Dazu District Mental Health Center from July 2021 to March 2023 were selected as the study subjects and divided into the observation group(n=38)and control group(n=39).The control group was treated with aripiprazole and olanzapine,and the observation group was combined with Ditan Decoction on the basis of the control group.After 8 weeks of treatment,the TCM syndrome scores,Positive and Negative Syndrome Scale(PANSS)score,serum inflammatory factors(IL-6,IL-1β,IL-17)levels were compared between the two groups.Results The total effective rate was 97.37％in the observation group and 84.65％in the control group,and the difference was statistically signifi-cant(P＜0.05).The TCM syndrome score of each item and total scores after treatment in the observation group were lower than those in the control group(P＜0.05),the PANSS positive symptoms,negative symp-toms,general psychopathology and total scores in the observation group were lower than those in the control group(P＜0.05).The IL-17,IL-6 and IL-1β levels after treatment in observation group were lower than those in the control group(P＜0.05).Conclusion Ditan Decoction combined with aripiprazole and olanzapine has significant clinical efficacy in the treatment of schizophrenia,which could further reduce the symptom score of the patients and improve the serum inflammatory factors levels.The treatment is highly safe and worthy of clinical recommendation.

Objective:To explore the application of the first ventilatory threshold (VT1) and the second ventilatory threshold (VT2) in the classification of physical workload for plateau workers, to provide reference for formulating the classification in plateau.Methods:In August 2018, 88 male workers from substations at different altitudes (500 m, 2000 m, 3000 m and 4000 m) of a company were selected as study subjects by cluster sampling. They were divided into plain group and plateau groups.The intensities of workload were simulated by power bicycle, and physiologic parameters, including VO 2, heart rate (HR) and energy metabolic rate per body surface area (E/BSA) , were recorded in test system when reaching VT1, VT2 and peak oxygen uptake (VO 2Peak) . The ratios of VT1, VT2 and VO 2Peak to the quiet and work potential at different altitudes were compared. Results:In a quiet state, compared with the plain group, the HR and E/BSA of the workers in the 2000 m and 3000 m groups increased, and the differences were statistically significant ( P<0.05) . At VT2, compared with the plain group, the HR of the workers in the 4000 m group decreased, the difference was statistically significant ( P<0.05) . VO 2 and E/BSA of workers in each plateau group were lower than those in the plain group at VO 2Peak, and the differences were statistically significant ( P<0.05) . At VT2 and VO 2Peak, the ratios of VO 2, HR, and E/BSA relative to the quiet state of the workers in each plateau group were lower than those of the plain group, and the differences were statistically significant ( P<0.05) . In the quiet state and VT1, compared with the plain group, the remaining percentages of VO 2 and E/BSA of workers in each plateau group decreased, and the differences were statistically significant ( P<0.05) . Taking VT1, VT2 and VO 2Peak as cut-off points and VO 2, HR and E/BSA as indicators, the physical workload in plateau could be divided into four levels, namely medium, heavy, extremely heavy and extreme physical workload. Conclusion:It is practicable to use ventilatory threshold to classification of physical workload. VT1 and VT2 can be applied to the classification in plateau to supplement and optimize current national standard of physical workload.

Objective:To explore the application of the first ventilatory threshold (VT1) and the second ventilatory threshold (VT2) in the classification of physical workload for plateau workers, to provide reference for formulating the classification in plateau.Methods:In August 2018, 88 male workers from substations at different altitudes (500 m, 2000 m, 3000 m and 4000 m) of a company were selected as study subjects by cluster sampling. They were divided into plain group and plateau groups.The intensities of workload were simulated by power bicycle, and physiologic parameters, including VO 2, heart rate (HR) and energy metabolic rate per body surface area (E/BSA) , were recorded in test system when reaching VT1, VT2 and peak oxygen uptake (VO 2Peak) . The ratios of VT1, VT2 and VO 2Peak to the quiet and work potential at different altitudes were compared. Results:In a quiet state, compared with the plain group, the HR and E/BSA of the workers in the 2000 m and 3000 m groups increased, and the differences were statistically significant ( P<0.05) . At VT2, compared with the plain group, the HR of the workers in the 4000 m group decreased, the difference was statistically significant ( P<0.05) . VO 2 and E/BSA of workers in each plateau group were lower than those in the plain group at VO 2Peak, and the differences were statistically significant ( P<0.05) . At VT2 and VO 2Peak, the ratios of VO 2, HR, and E/BSA relative to the quiet state of the workers in each plateau group were lower than those of the plain group, and the differences were statistically significant ( P<0.05) . In the quiet state and VT1, compared with the plain group, the remaining percentages of VO 2 and E/BSA of workers in each plateau group decreased, and the differences were statistically significant ( P<0.05) . Taking VT1, VT2 and VO 2Peak as cut-off points and VO 2, HR and E/BSA as indicators, the physical workload in plateau could be divided into four levels, namely medium, heavy, extremely heavy and extreme physical workload. Conclusion:It is practicable to use ventilatory threshold to classification of physical workload. VT1 and VT2 can be applied to the classification in plateau to supplement and optimize current national standard of physical workload.

OBJECTIVEProviding evidences for further modification of China Infectious Diseases Automated-alert and Response System (CIDARS) via analyzing the outbreak detection performance of Moving Percentile Method (MPM) by optimizing thresholds in different provinces.

METHODSWe collected the amount of MPM signals, response results of signals in CIDARS, cases data in nationwide Notifiable Infectious Diseases Reporting Information System, and outbreaks data in Public Health Emergency Reporting System of 16 infectious diseases in 31 provinces in Chinese mainland from January 2011 to October 2013. The threshold with the optimal sensitivity, the shortest time to detect outbreak and the least number of signals was considered as the best threshold of each disease in Chinese mainland and in each province.

RESULTSAmong all the 16 diseases, the optimal thresholds of 10 diseases, including dysentery, dengue, hepatitis A, typhoid and paratyphoid, meningococcal meningitis, Japanese encephalitis, scarlet fever, leptospirosis, hepatitis, typhus in country level were the 90(th) percentile (P90), which was the same as provincial level for those diseases.For the other 6 diseases, including other infectious diarrhea, influenza, acute hemorrhagic conjunctivitis, mumps, rubella and epidemic hemorrhagic fever, the nationwide optimal thresholds were the 80th percentile (P80), which was different from that by provinces for each disease. For these 6 diseases, the number of signals generated by MPM with the optimal threshold for each province was decreased by 23.71% (45 557), 15.59% (6 124), 14.07% (1 870), 9.44% (13 881), 8.65% (1 294) and 6.03% (313) respectively, comparing to the national optimal threshold, while the sensitivity and time to detection of CIDARS were still the same.

CONCLUSIONOptimizing the threshold by different diseases and provinces for MPM in CIDARS could reduce the number of signals while maintaining the same sensitivity and time to detection.

China ; Communicable Diseases ; Disease Notification ; Disease Outbreaks ; prevention & control ; Humans ; Population Surveillance ; methods