This study aims to clarify host factors of IFN treatment in the treatment of chronic hepatitis B (CHB) patients by screening the differentially expressed genes of IFN pathway CHB patients with different response to interferon (IFN) therapy. Three cases were randomly selected in IFN-responding CHB patients (Rs), non-responding CHB patients (NRs) and healthy participants, respectively. The human type I IFN response RT2 profiler PCR array was used to detect the expression levels of IFN-related genes in peripheral blood monocytes (PBMCs) from healthy participants and CHB patients before and after Peg-IFN-α 2a treatment. The results showed that more differentially expressed genes appeared in Rs group than NRs group after IFN treatment. Comparing with healthy participants, IFNG, IL7R, IRF1, and IRF8 were downregulated in both Rs and NRs group before IFN treatment; CXCL10, IFIT1, and IFITM1 were upregulated in the Rs; IL13RA1 and IFI35 were upregulated in the NRs, while IFRD2, IL11RA, IL4R, IRF3, IRF4, PYHIN1, and ADAR were downregulated. The expression of IL15, IFI35 and IFI44 was downregulated by 4.09 (t = 10.58, P < 0.001), 5.59 (t = 3.37, P = 0.028) and 10.83 (t = 2.8, P = 0.049) fold in the Rs group compared with the NRs group, respectively. In conclusion, IFN-response-related gene array is able to evaluate IFN treatment response by detecting IFN-related genes levels in PBMC. High expression of CXCL10, IFIT1 and IFITM1 before treatment may suggest satisfied IFN efficacy, while high expression of IL13RA1, IL15, IFI35 and IFI44 molecules and low expression of IFRD2, IL11RA, IL4R, IRF3, IRF4, PYHIN1 and ADAR molecules may be associated with poor IFN efficacy.
ObjectiveTo explore the cytologic profile of induced sputum and its relationship with the treatment response in patients with chronic obstructive pulmonary disease (COPD). MethodsSixty-five treatment-naive patients with COPD and 26 normal subjects were recruited for the study. Sputums induced by the inhalation of hypertonic saline were collected, and the associations of differential cell counting were analyzed with pulmonary function, modified Medical Research Council dyspnea scale, St. George's Respiratory Questionnaire score (SGRQ) before and after the treatment with inhaled corticosteroid and long-acting β2-agonist. ResultsThe cell percentages of neutrophil (Neu), macrophage, eosinophil (Eos) and lymphocyte in induced sputum of the COPD patients were (86.24±15.04)%, (5.75±6.96)%, (4.71±4.79)%, and (1.30±1.09)%, respectively. The eosinophil percentage (Eos%) was≥3% in 31 patients (60.78%). The neutrophil percentage (Neu%) was inversely correlated with forced expiratory volume in 1 second (FEV1), percent of predicted value of FEV1 (FEV1% pred), forced vital capacity (FVC), and percent of predicted value of FVC (FVC% pred) (P < 0.01, respectively), and positively correlated with the SGRQ symptom score (r=0.304, P=0.034). The Eos% was inversely correlated with FEV1/FVC ratio (r=-0.399, P=0.004). The patients with Eos%≥3% improved significantly in FEV1 and symptom score (P < 0.05, respectively) than the patients with Eos% < 3%. ConclusionsAn eosinophilic airway inflammation is present in a subgroup of COPD. The Eos% is inversely correlated with pulmonary function and may be a predictive indicator of response to treatment with inhaled corticosteroids and long-acting β2-agonists.
With the rising incidence of breast cancer among women, neoadjuvant chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting pathological complete response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.