Impact of Health Education Based on Protective Motivation Theory on the Mood State, Cancer-Related Fatigue, and Hope Level of Gastric Cancer Patients.

Background: Gastric cancer patients often feel physically tired and weak, lacking confidence and enthusiasm for relevant treatments. We aimed to explore the impacts of health education based on the theory of protective motivation on the emotional state, cancer-related fatigue, and hope levels of gastric cancer patients. Methods: A total of 160 gastric cancer patients admitted to the Sanmenxia Central Hospital, Henan, China, from May 2019 to March 2022 were selected as subjects. The control group (n=80) received routine health education, while the observation group (n=80) received health education based on the theory of protective motivation. Intervention evaluations included the Morisky medication compliance score, Plain Mood State Scale (POMS), Cancer Fatigue Scale (CFS), Herth Hope Scale (HHI), and Simple Health Survey Scale (SF-36). Results: After intervention, both groups showed an improvement in Morisky's medication compliance score, HHI scale score, and SF-36 scale score (all P<0.05). Additionally, the observation group exhibited greater improvement than the control group (P<0.05). There were no significant differences in POMS scale score and CFS scale score between the two groups before and after intervention. However, after intervention, both groups experienced a decrease in POMS scale score and CFS scale score (both P<0.05), with the observation group showing a more significant decrease compared to the control group (P<0.05). Conclusion: Health education based on the theory of protective motivation effectively enhances the mood state, reduces cancer-related fatigue, and increases hope levels among gastric cancer patients, thereby improving their medication compliance and overall quality of life.

Image Risk Assessment of the Thyroid Cancer Model Based on Discriminant Analysis and the Value of TAP and CEA Combined Detection.

The incidence rate of thyroid disease is increasing rapidly worldwide, and the number of thyroid patients is increasing. In this study, serum TAP (tumor abnormal protein) and CEA (carcinoembryonic antigen) were used to detect patients with thyroid nodules of class IV and above to explore the value of serum TAP combined detection of CEA in the risk assessment of thyroid cancer. In this paper, 400 patients with thyroid nodules above class IV diagnosed by physical examination in our hospital health management center from January 2019 to June 2021 were included in the study. Combined with the pathological test results, the patients were divided into risk groups. At the same time, different groups of serum TAP and CEA levels were detected by aggregation and electrochemiluminescence methods, and serum TAP and CEA levels were analyzed according to the pathological diagnostic indicators of CEA levels. The results showed that the levels of serum TAP and CEA in patients with thyroid cancer were significantly higher than those in patients with benign thyroid diseases, and the difference was statistically significant (P < 0.05). The sensitivity, specificity, and AUC under the ROC curve area of serum TAP were 85.25%, 85.06%, and 0.605, respectively. The sensitivity, specificity, and AUC under the ROC curve area of serum CEA were 89.85%, 88.00%, and 0.627, respectively. The sensitivity, specificity, and AUC under the ROC curve area of serum TAP combined with CEA were 96.84%, 96.79%, and 0.915, respectively. Therefore, the combined detection of serum TAP and CEA has a high early screening value in thyroid cancer.

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer.

In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnosis due to their cheapness, no radiation, and low cost. The use of image processing technology to accurately segment the nodule area provides important auxiliary information for the doctor's diagnosis, which is of great value for guiding clinical treatment. The purpose of this article is to explore the application value of combined detection of abnormal sugar-chain glycoprotein (TAP) and carcinoembryonic antigen (CEA) in the risk estimation of thyroid cancer in patients with thyroid nodules of type IV and above based on deep learning medical images. In this paper, ultrasound thyroid images are used as the research content, and the active contour level set method is used as the segmentation basis, and a segmentation algorithm for thyroid nodules is proposed. This paper takes ultrasound thyroid images as the research content, uses the active contour level set method as the basis of segmentation, and proposes an image segmentation algorithm Fast-SegNet based on deep learning, which extends the network model that was mainly used for thyroid medical image segmentation to more scenarios of the segmentation task. From January 2019 to October 2020, 400 patients with thyroid nodules of type IV and above were selected for physical examination and screening at the Health Management Center of our hospital, and they were diagnosed as thyroid cancer by pathological examination of thyroid nodules under B-ultrasound positioning. The detection rates of thyroid cancer in patients with thyroid nodules of type IV and above are compared; serum TAP and CEA levels are detected; PT-PCR is used to detect TTF-1, PTEN, and NIS expression; the detection, missed diagnosis, misdiagnosis rate, and diagnostic efficiency of the three detection methods are compared. This article uses the thyroid nodule region segmented based on deep learning medical images and compares experiments with CV model, LBF model, and DRLSE model. The experimental results show that the segmentation overlap rate of this method is as high as 98.4%, indicating that the algorithm proposed in this paper can more accurately extract the thyroid nodule area.

New Delhi Metallo-ß-Lactamase 1(NDM-1), the Dominant Carbapenemase Detected in Carbapenem-Resistant Enterobacter cloacae from Henan Province, China.

The emergence of New Delhi metallo-ß-lactamase 1 (NDM-1) has become established as a major public health threat and represents a new challenge in the treatment of infectious diseases. In this study, we report a high incidence and endemic spread of NDM-1-producing carbapenem-resistant Enterobacter cloacae isolates in Henan province, China. Eight (72.7%) out of eleven non-duplicated carbapenem-resistant E. cloacae isolates collected between June 2011 and May 2013 were identified as NDM-1 positive. The blaNDM-1 gene surrounded by an entire ISAba125 element and a bleomycin resistance gene bleMBL in these isolates were carried by diverse conjugatable plasmids (IncA/C, IncN, IncHI2 and untypeable) ranging from ~55 to ~360 kb. Molecular epidemiology analysis revealed that three NDM-1-producing E. cloacae belonged to the same multilocus sequence type (ST), ST120, two of which were classified as extensively drug-resistant (XDR) isolates susceptible only to tigecycline and colistin. The two XDR ST120 E. cloacae isolates co-harbored blaNDM-1, armA and fosA3 genes and could transfer resistance to carbapenems, fosfomycin and aminoglycosides simultaneously via a conjugation experiment. Our study demonstrated NDM-1 was the most prevalent metallo-ß-lactamase (MBL) among carbapenem-resistant E.cloacae isolates and identified a potential endemic clone of ST120 in Henan province. These findings highlight the need for enhanced efforts to monitor the further spread of NDM-1 and XDR ST120 E. cloacae in this region.