Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

Objective To investigate the effect of sal-like gene 2(Sall2)gene overexpression on the progression of disease in human superoxide dismutase 1(hSOD1)-G93A mutant amyotrophic lateral sclerosis(ALS)transgenic mice,with the aim of identifying potential therapeutic targets for ALS gene therapy.Methods Differential Sall2 gene were screened through bioinformatics analysis.Forty-eight ALS transgenic mice were selected for this study.AAV-PHP.eB-Sall2 adeno-associated virus with a neuron-specific promoter,human synapsin I(hSyn),was constructed and administered via tail vein injection to six-week-old mice.In parallel,the same litter of ALS mice received an injection of AAV-PHP.eB-GFP.The staining of Sall2 and neuron-specific nuclear protein(NeuN)/GFAP in the spinal cord and cerebral cortex of mice were detected through immunofluorescent double-label staining technology.The survival period,weight changes,exercise ability,and electromyographic changes of the gastrocnemius muscle were detected.The morphological changes in the spinal cord anterior horn neurons were detected through Nissl staining.The effect of Sall2 gene overexpression on the expression of the cell cycle protein E1(cyclin E1)was investigated through Western blotting.Results Bioinformatics analysis showed out that Sall2 was differentially expressed in ALS mice.Compared with ALS mice in the control group,the Sall2 protein expression of ALS mice in the overexpressing Sall2 gene group increased in both the spinal cord and cerebral cortex,and the Sall2 integral absorbance values of Sall2+/NeuN+double-positive cells were higher.The survival time of ALS mice in the Sall2 gene overexpressing group was prolonged,the rate of weight loss was slowed down,the performance in the rotarod and inverted grid tests was improved with longer times,and the positive sharp waves and fibrillation potentials in the gastrocnemius electromyography were reduced.The number of Nissl bodies labeled neurons increased in the spinal cord anterior horn of the Sall2 gene overexpressing mice,and the condition of neuronal damage was improved.Overexpression of the Sall2 gene also reduced the expression of cyclin E1 in both the spinal cord and cerebral cortex of ALS transgenic mice.Conclusion Overexpression of the Sall2 gene can delay disease progression and improve motor performance in ALS transgenic mice,affecting the expression of cyclin E1,thus exerting a therapeutic effect on these mice.

Objective To investigate the expression of myelin transcription factor 1-like(MYT1L)during amyotrophic lateral sclerosis(ALS)progression and its association with neuronal degeneration through bioinformatics analysis combined with in vivo and in vitro experiments.Methods Bioinformatics analysis of the GSE106803 dataset from the Gene Expression Omnibus(GEO)database revealed significant down-regulation of MYT1L in spinal cords of ALS transgenic mice carrying the human superoxide dismutase 1 mutant gene(hSOD1G93A)compared to the wild-type(WT)mice.hSOD1G93A transgenic mice and their WT littermates were selected to analyze MYT1L mRNA and protein changes in spinal cord tissues at different disease stages using Real-time PCR and Western blotting.Double immunofluorescent staining was used to determine the distribution and cellular localization of MYT1L in the spinal cord of mice at the middle stage of the disease.An ALS cellular model was established using hSOD1G93A mutant NSC34 cells,with hSOD1WT NSC34 cells as controls.MYT1L expression and distribution were assessed in these cells via Real-time PCR,Western blotting,and immunofluorescent staining.Based on the GSE76220 dataset from the GEO database,differentially expressed genes(DEGs)between MYT1L high-and low-expression groups in lumbar spinal motor neurons of ALS patients were identified,followed by Gene Ontology(GO)functional enrichment analysis.MYT1L overexpression was induced in the ALS cellular model to evaluate alterations in cell viability and neurite outgrowth.Results In the GSE106803 dataset,MYT1L expression was significantly down-regulated in the spinal cord of ALS mice.Animal experiments confirmed progressive reductions in MYT1L mRNA and protein levels in spinal cord tissues of ALS mice during mid-and late-disease stages.Compared to the WT group,MYT1L expression decreased in motor neurons of the lumbar spinal cord gray matter anterior horn in ALS mice,while it increased in astrocytes.In vitro,hSOD1G93Amutant NSC34 cells exhibited significantly reduced MYT1L expression than controls,with MYT1L localized to both the cytoplasm and nucleus.DEGs between MYT1L high-and low-expression groups in lumbar spinal cord motor neurons of ALS patients(GSE76220 dataset)were enriched in synaptic-related functions through GO analysis.Overexpression of MYT1L in hSOD1G93A mutant NSC34 cells enhanced cell viability and promoted neurite outgrowth.Conclusion Aberrantly low expression of MYT1L is closely associated with ALS pathogenesis.Overexpression of MYT1L promotes neurite growth and exerts protective effects on ALS motor neurons,suggesting its therapeutic potential.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

OBJECTIVE: To analyze the factors influencing the development of refeeding syndrome (RFS) in patients with sepsis and its impact on clinical prognosis. METHODS: A retrospective case-control study method was used to collect the clinical data of patients with sepsis admitted to the intensive care unit (ICU) of Qingdao Municipal Hospital from December 2018 to December 2023. The patients were divided into RFS and non-RFS groups according to whether RFS occurred, and the basic data, nutritional status and assessment scale, laboratory indicators, nutritional intake, medical history and prognosis were compared between the two groups. Binary multifactorial Logistic regression analysis was used to screen the influencing factors of the occurrence of RFS in patients with sepsis. RESULTS: A total of 544 patients with sepsis were finally enrolled, of whom 250 did not develop RFS and 294 developed RFS, with an incidence of 54.0%. Compared with the non-RFS group, the patients in the RFS group had lower body mass index (BMI), albumin, prealbumin, baseline electrolytes (serum phosphorus, serum potassium, and serum magnesium), creatinine-height index, and protein intake, and had higher nutritional risk screening 2002 (NRS2002) score, sequential organ failure assessment (SOFA) score, calorie intake, and the proportions of feedings during the 48 hours of ICU admission, history of diabetes and septic shock. Binary multifactorial Logistic regression analysis showed that BMI [odds ratio (OR) = 0.910, 95% confidence interval (95%CI) was 0.857-0.947, P < 0.001], SOFA score (OR = 1.166, 95%CI was 1.085-1.254, P < 0.001), albumin (OR = 0.946, 95%CI was 0.902-0.991, P = 0.019), baseline serum phosphorus (OR = 0.343, 95%CI was 0.171-0.689, P = 0.003), baseline serum potassium (OR = 0.531, 95%CI was 0.377-0.746, P < 0.001), creatinine-height index (OR = 0.891, 95%CI was 0.819-0.970, P = 0.008), caloric intake (OR = 1.108, 95%CI was 1.043-1.178, P = 0.001), protein intake (OR = 0.107, 95%CI was 0.044-0.260, P < 0.001), and feedings during the 48 hours of ICU admission (OR = 0.592, 95%CI was 0.359-0.977, P = 0.040) and septic shock (OR = 0.538, 95%CI was 0.300-0.963, P = 0.037) were independent influence factors on the occurrence of RFS in septic patients. Of the 544 patients, 267 died at 28 days, with a mortality of 49.1%. The 28-day mortality of patients in the RFS group was significantly higher than that in the non-RFS group [54.4% (160/294) vs. 42.8% (107/250); χ² = 7.302, P = 0.007]. 544 patients had a length of ICU stay of 20 (17, 24) days. The patients in the RFS group had a significantly longer length of ICU stay than that in the non-RFS group [days: 20 (17, 25) vs. 19 (17, 23); Z = -2.312, P = 0.021]. CONCLUSIONS The incidence of RFS in septic patients is high. Factors influencing the occurrence of RFS in septic patients include BMI, SOFA score, albumin, baseline serum phosphorus, baseline serum potassium, caloric intake, protein intake, feeding within 48 hours of ICU admission, and septic shock. RFS prolongs the length of ICU stay and increases the 28-day mortality in patients with sepsis.
Humans ; Retrospective Studies ; Sepsis/complications* ; Prognosis ; Refeeding Syndrome/etiology* ; Case-Control Studies ; Intensive Care Units ; Male ; Nutritional Status ; Female ; Risk Factors ; Middle Aged ; Logistic Models ; Body Mass Index ; Aged

Objective:To observe the application value of Yifei Fumai Tang in the treatment of difficulty offline and intensive care unit-acquired weakness(ICU-AW).Methods:Used random number table method,60 patients with difficulty offline and ICU-AW who were admitted to our hospital from June 2022 to June 2024 were divided into control group(received conventional Western medicine comprehensive treatment,n=30)and study group(received Yifei Fumai Tang treatment in addition to the control group,n=30).Medical Research Council(MRC),Barthel Index(BI)scores,clinical indicators(total mechanical ventilation time,weaning success rate,secondary intubation rate,ICU stay,and mobilization time),diaphragm thickness,diaphragm mobility,and incidence of adverse reactions between two groups were compared.Results:Compared with the control group at 7 d after treatment and 14 d after treatment,the MRC and BI scores in the study group were higher(P＜0.05).Compared with the control group,the study group had lower rate of secondary intubation rate,shorter total mechanical ventilation time,ICU stay time,and mobilization time,higher weaning success rate,and greater diaphragm thickness,diaphragm mobility(P＜0.05).There was no significant difference in the incidence of adverse reactions between the two groups(P＞0.05).Conclusion:Yifei Fumai Tang combined with Western Medicine in the treatment of Difficulty Offline and ICU-AW,can effectively improve the prognosis of patients,shorten total mechanical ventilation time,ICU stay,mobilization time,increase weaning success rate,and improve respiratory function.

With the latest development of information and communication technology, mobile applications for health care have attracted much attention to help cancer patients better manage their diseases. Digital therapeutics is an evidence-based intervention driven by high-quality software programs that prevent, manage, or treat medical disorders or diseases. This paper summarizes the origin, concept and characteristics of digital therapeutics and the application form, effect, challenge and opportunity of symptom management in cancer patients, in order to provide reference for the scientific and efficient application of Digital therapeutics to cancer patients.

Objective:To observe the application value of Yifei Fumai Tang in the treatment of difficulty offline and intensive care unit-acquired weakness(ICU-AW).Methods:Used random number table method,60 patients with difficulty offline and ICU-AW who were admitted to our hospital from June 2022 to June 2024 were divided into control group(received conventional Western medicine comprehensive treatment,n=30)and study group(received Yifei Fumai Tang treatment in addition to the control group,n=30).Medical Research Council(MRC),Barthel Index(BI)scores,clinical indicators(total mechanical ventilation time,weaning success rate,secondary intubation rate,ICU stay,and mobilization time),diaphragm thickness,diaphragm mobility,and incidence of adverse reactions between two groups were compared.Results:Compared with the control group at 7 d after treatment and 14 d after treatment,the MRC and BI scores in the study group were higher(P＜0.05).Compared with the control group,the study group had lower rate of secondary intubation rate,shorter total mechanical ventilation time,ICU stay time,and mobilization time,higher weaning success rate,and greater diaphragm thickness,diaphragm mobility(P＜0.05).There was no significant difference in the incidence of adverse reactions between the two groups(P＞0.05).Conclusion:Yifei Fumai Tang combined with Western Medicine in the treatment of Difficulty Offline and ICU-AW,can effectively improve the prognosis of patients,shorten total mechanical ventilation time,ICU stay,mobilization time,increase weaning success rate,and improve respiratory function.

With the latest development of information and communication technology, mobile applications for health care have attracted much attention to help cancer patients better manage their diseases. Digital therapeutics is an evidence-based intervention driven by high-quality software programs that prevent, manage, or treat medical disorders or diseases. This paper summarizes the origin, concept and characteristics of digital therapeutics and the application form, effect, challenge and opportunity of symptom management in cancer patients, in order to provide reference for the scientific and efficient application of Digital therapeutics to cancer patients.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.