Objective To analyze the serum of patients with pancreatic cancer by fourier transform infrared spectroscopy(FTIR),explore the characteristic spectral parameters related to pancreatic cancer,and evaluate its potential clinical diagnostic value.Methods Serum samples were collected from 100 patients diagnosed with pancreatic cancer and 92 healthy volunteers at Chongming Hospital Affiliated to Shanghai Health Medical College from August 2022 to July 2023.These samples underwent FTIR and principal component analysis(PCA)to assess spectral differences between the two cohorts.The diagnostic potential of the serum spectra in distinguishing pancreatic cancer patients from healthy individuals was further evaluated using machine learning techniques,specifically support vector machine(SVM),k-nearest neighbor(kNN),and linear discriminant analysis(LDA)as classification methods.The diagnostic efficacy across various thresholds was evaluated using the receiver operating characteristic(ROC)curve.Results The findings indicate that the peak positions within the 1 090～1 070cm-1(1 076.537±15.183cm-1 vs 1 081.061±4.043cm-1),1 420～1 380 cm-1(1 399.958±1.508cm-1 vs 1 400.500±1.782cm-1),2 990～2 950cm-1(2 940.167±15.287cm-1 vs 2 945.124±7.498cm-1)and 3 500～3 000 cm-1(3 293.155±3.096cm-1 vs 3 294.893±2.582cm-1)range in the serum of individuals with pancreatic cancer exhibited a significant blue-shift compared to the healthy group and was statistically significant(t=2.265～4.236,all P<0.05),suggesting alterations in the structures of proteins,lipids and nucleic acids.Furthermore,a statistically significant disparity in peak absorption was observed between the group of patients with pancreatic cancer and the healthy group within the spectral ranges of 1 700～1 600cm-1(0.918±0.012cm-1 vs 0.858±0.021cm-1)and 3 500～3 000 cm-1(0.766±0.096cm-1 vs 0.804±0.090cm-1)(t=-24.031,2.830,all P<0.05),indicating that the protein concentration changes.PCA results showed that the PC2 axis was clearly separated,which could distinguish serum samples from patients with pancreatic cancer.Utilizing a machine learning model to differentiate the serum spectra of patients with pancreatic cancer from those of healthy controls,the sensitivity,the spesitivity,the specificity and accuracy of linear discriminant analysis(LDA)classification method were 93.2%,97.3%and 95.8%,respectively.The area under curve(AUC)as determined by ROC analysis was 0.982.Conclusion Serum spectroscopy using FTIR combined with PCA and machine learning model can be a simple,minimally invasive and reliable diagnostic test for pancreatic cancer detection.

Objective To analyze the serum of patients with pancreatic cancer by fourier transform infrared spectroscopy(FTIR),explore the characteristic spectral parameters related to pancreatic cancer,and evaluate its potential clinical diagnostic value.Methods Serum samples were collected from 100 patients diagnosed with pancreatic cancer and 92 healthy volunteers at Chongming Hospital Affiliated to Shanghai Health Medical College from August 2022 to July 2023.These samples underwent FTIR and principal component analysis(PCA)to assess spectral differences between the two cohorts.The diagnostic potential of the serum spectra in distinguishing pancreatic cancer patients from healthy individuals was further evaluated using machine learning techniques,specifically support vector machine(SVM),k-nearest neighbor(kNN),and linear discriminant analysis(LDA)as classification methods.The diagnostic efficacy across various thresholds was evaluated using the receiver operating characteristic(ROC)curve.Results The findings indicate that the peak positions within the 1 090～1 070cm-1(1 076.537±15.183cm-1 vs 1 081.061±4.043cm-1),1 420～1 380 cm-1(1 399.958±1.508cm-1 vs 1 400.500±1.782cm-1),2 990～2 950cm-1(2 940.167±15.287cm-1 vs 2 945.124±7.498cm-1)and 3 500～3 000 cm-1(3 293.155±3.096cm-1 vs 3 294.893±2.582cm-1)range in the serum of individuals with pancreatic cancer exhibited a significant blue-shift compared to the healthy group and was statistically significant(t=2.265～4.236,all P<0.05),suggesting alterations in the structures of proteins,lipids and nucleic acids.Furthermore,a statistically significant disparity in peak absorption was observed between the group of patients with pancreatic cancer and the healthy group within the spectral ranges of 1 700～1 600cm-1(0.918±0.012cm-1 vs 0.858±0.021cm-1)and 3 500～3 000 cm-1(0.766±0.096cm-1 vs 0.804±0.090cm-1)(t=-24.031,2.830,all P<0.05),indicating that the protein concentration changes.PCA results showed that the PC2 axis was clearly separated,which could distinguish serum samples from patients with pancreatic cancer.Utilizing a machine learning model to differentiate the serum spectra of patients with pancreatic cancer from those of healthy controls,the sensitivity,the spesitivity,the specificity and accuracy of linear discriminant analysis(LDA)classification method were 93.2%,97.3%and 95.8%,respectively.The area under curve(AUC)as determined by ROC analysis was 0.982.Conclusion Serum spectroscopy using FTIR combined with PCA and machine learning model can be a simple,minimally invasive and reliable diagnostic test for pancreatic cancer detection.

?? [Abstract]? Objective? To explore the current status of dyadic coping among pelvic floor dysfunction (PFD) patients and their spouses so as to provide a new perspective for clinical medical staff to carry out PFD family prevention intervention contrapuntally. Methods? From March to December 2017, we selected totals of 150 pelvic floor dysfunction patients and their spouses in the Second Affiliated Hospital of Wenzhou Medical University by convenience sampling. All of the patients were investigated with the general information questionnaire and Dyadic Coping Inventory (DCI). Totals of 150 questionnaires were sent out to patients and their spouses, and 138 valid of them from patients and their spouses were collected with 92.0% for the valid recovery rate. Results? The score of pressure communication of PFD patients was lower than that of their spouses, and the score of negative coping of PFD patients was higher than that of their spouses with statistical differences (P＜ 0.05). Pearson correlation analysis showed that the scores of dyadic coping of PFD patients had positive correlations with those of their spouses (P＜ 0.05), and the scores of negative coping of PFD patients and their spouses had negative correlations with the scores of pressure communication, support, authorized coping and cooperation (P＜ 0.05). Conclusions? PFD patients and their spouses are with obvious negative coping and inadequate dyadic coping. Besides, dyadic coping of patients is closely related to dyadic support. Clinical medical staff should stress the importance of dyadic coping, and encourage PFD patients and their spouses to actively participate in and face disease together and to relieve stress.