Objective To investigate the common diseases and the causes of diseases in Chinese Antarctic researchers,so as to provide reference for the medical support.Methods Medical records of 1 127 people with injuries and diseases who participated in four Antarctic scientific expeditions(the 31st,35th,36th,and 39th time)were retrospectively analyzed.The causes of the injuries and diseases as well as the implications for nursing were investigated.Results The top 10 diseases in the four Antarctic expeditions were acute soft tissue injury,dermatomycosis,pharyngitis,insomnia,periodontitis,gastroenteritis,motion sickness,acute upper respiratory infection,primary hypertension,and frostbite.The causes of the diseases in the four Antarctic expeditions were analyzed,and the nursing of different diseases was proposed.Conclusion It is necessary to take preventive measures based on the characteristics of injuries and diseases during Antarctic expeditions,so as to effectively prevent and treat these diseases and provide more comprehensive medical support for Antarctic scientific expeditions.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Executive function(EF) is an advanced cognitive function of the central nervous system, and is closely related to an individual's capacity for daily living and adaptation. Patients with attention deficit hyperactivity disorder (ADHD) typically exhibit significant executive dysfunction. While most existing studies on the executive function of individuals with ADHD are cross-sectional, and little is known about the longitudinal maturation process of related brain structures and functional connectivity patterns. The findings indicate that ADHD patients exhibit differential developmental trajectories in brain structural and functional connectivity compared with typically developing group.Furthermore, there is a lifespan association between abnormal brain network development and ADHD symptoms. This article aims to elucidate the characteristics of executive function deficits in ADHD patients across different developmental stages, examining their relationship with the nervous system’s development from a development perspective.

Executive function(EF) is an advanced cognitive function of the central nervous system, and is closely related to an individual's capacity for daily living and adaptation. Patients with attention deficit hyperactivity disorder (ADHD) typically exhibit significant executive dysfunction. While most existing studies on the executive function of individuals with ADHD are cross-sectional, and little is known about the longitudinal maturation process of related brain structures and functional connectivity patterns. The findings indicate that ADHD patients exhibit differential developmental trajectories in brain structural and functional connectivity compared with typically developing group.Furthermore, there is a lifespan association between abnormal brain network development and ADHD symptoms. This article aims to elucidate the characteristics of executive function deficits in ADHD patients across different developmental stages, examining their relationship with the nervous system’s development from a development perspective.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

In the context of "New Era" and "New Medicine", the teaching team of laboratory diagnostics proposed a new teaching method of PDCA (Plan, Do, Check, Action) cycle to innovate the instructional design and implementation of laboratory diagnosis on the base of traditional teaching model. Through optimizing the curriculum system, integrating superior resources, taking full advantage of information technology, mobilizing the virtual simulation laboratory, implementing moral education and multiple comprehensive evaluation results, the self-learning enthusiasm, innovation and development ability of students were improved significantly, accompanied with high humanistic care spirit. Moreover, the enthusiasm of participating in scientific research activities and professional competitions was enhanced obviously. Therefore, we believe that the PDCA cycle, with notable successes, deserves high promotion value and wide application.

Digital therapeutics(DTs)refers to a non-drug intervention method that uses electronic devices such as computers,smartphones,and wearable devices to evaluate and intervene through software programs and Internet technologies.It has been confirmed that there is a good therapeutic effect on a variety of mental disorders.Digital therapeutics can improve the insomnia problems of insomniacs,enhance the attention and work memory ability of patients with attention deficit hyperactivity disorder,and can also alleviate symptoms such as depression and anxiety disorder.Digital therapy will develop towards personalized treatment,popular treatment,fragmented treatment,and entertainment treatment in the future and have broad development prospects.

Chronic kidney disease (CKD) is a serious health problem worldwide, whereas there is still no efficient cure. The gut microbiota plays a crucial role in maintaining human health and disease resistance, and multiple studies have confirmed that the gut microbiota is closely related to the occurrence and development of CKD. Starting from the "gut-kidney axis" theory, this article provides a systematic review of the changes in gut microbiota composition and function in patients with CKD, such as a decrease in the abundance of butyrate-producing bacteria Roseburia and Faecalibacterium prausnitzii. Besides that, the article explores the mechanisms by which the gut microbiota affects CKD progression, such as inflammation and immunity, and also describes the application methods of using the gut microbiota as a therapeutic target for CKD, such as fecal microbiota transplantation, microecologics, and dietary therapy, in order to provide microbial- based targets for the clinical diagnosis and treatment of CKD.

Human milk oligosaccharides (HMO) are important immunoactive components found in breast milk. Scientific research proves that HMOs are significantly beneficial for infant health. 2'-fucosyllactose (2'-FL) is the major component of HMO, which obtained growing attentions from food industry. Besides, 3-fucosyllactose (3-FL) is another important fucosyllactose and it has a similar synthetic route comparing to 2'-FL. Thus, research of the two HMO components has interactive effects for each other. Recently, numerous publications are available for 2'-FL and 3-FL. The microbial cell factory is able to massively produce fucosyllactose via an efficient way, which will show considerable influences in dairy industry. In this paper, we review recent studies on 2'-FL and 3-FL, and discuss their prospects according to published literature and patents.
Female ; Humans ; Infant ; Milk, Human ; Oligosaccharides ; Trisaccharides

Objective To investigate the difference of niacin skin flush response between patients with major depressive disorder (MDD) and healthy controls (HCs), and its sensitivity and specificity for the diagnosis of MDD. Methods Twenty-one untreated patients with MDD and 28 age- and gender-matched HCs were enrolled in this study. The severity of depressive symptoms was assessed mainly by using the 17-item Hamilton Depression Rating Scale (HDRS-17). Methyl Nicotinate (MN) solution at 8 different concentrations (10-5 mol/L, 10-4 mol/L, 10-3.5 mol/L, 10-3 mol/L, 10-2.5 mol/L, 10-2 mol/L, 10-1.5 mol/L, 10-1 mol/L) were applied on subjects' forearms. Signals of blood flow were collected using the Doppler Laser Flowmetry to detect the skin flushing of the test. Results Under the concentrations of 10-2.5 mol/L, 10-2 mol/L, 10-1.5 mol/L and 10-1 mol/L MN solution, the blood flow was significantly higher in depressive patients than in HCs (P<0.01). The MN sensitivity (logEC50) was inversely correlated to the severity of depressive symptoms (r=-0.57, P<0.05). ROC curve analysis implied that the maximum blood flow (MBF) caused by the niacin skin flush response, could efficiently discriminate MDD from HCs (AUC=0.90, P<0.01). Conclusion The presence of enhanced niacin skin flush response may be helpful in the diagnosis of MDD.