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Abstract Squamous cell carcinoma (SCC) is well-known for its high rate of metastasis with poor prognosis. CD109 is a glycosylphosphatidylinositol-anchored cell-surface glycoprotein. Recently, CD109 emerges as a potential biomarker and a therapeutic target for SCCs. Accumulating studies have reported that CD109 is highly expressed in human SCCs of multiple organs, and may contribute to the progression of SCCs. In this review, we summarized the findings on expression pattern of CD109 in SCCs, and discussed the molecular mechanisms underlying the roles of CD109 in pathogenesis of SCCs.

Abstract Background With the increasing number of bariatric surgeries, the high incidence of postoperative nausea and vomiting (PONV) associated with this surgery has also gradually attracted attention. Among the common bariatric surgery methods, patients undergoing sleeve gastrectomy (SG) have the highest incidence of nausea and vomiting. The mechanism of occurrence of PONV is very complex. This study aims to explore the influencing factors of PONV in patients undergoing laparoscopic sleeve gastrectomy (LSG) and construct a nomogram prediction model based on these factors. Methods With the approval of the Ethics Committee, the electronic medical records of patients who underwent LSG from July 2022 to May 2023 were collected retrospectively. Results A total of 114 patients with complete medical records who underwent LSG from July 2022 to May 2023 were included in this study. Among them, 46 patients developed PONV, resulting in a PONV incidence rate of 40.4%. Multivariate logistic regression analysis revealed that female gender, the use of inhalation anesthesia, and operation time ≥ 120 min were risk factors for PONV in LSG. Additionally, the use of more than two kinds of antiemetic drugs was identified as a protective factor. Based on these factors, a nomogram model was constructed. Conclusion PONV in patients undergoing LSG is related to gender, type of anesthesia, duration of surgery, and combination therapy with antiemetic drugs. The nomogram prediction model constructed in this study demonstrates high accuracy and discrimination in predicting the occurrence of PONV in patients undergoing LSG.

Laboratory and field experiments were performed to examine the feasibility of using Time Domain Reflectometry (TDR) to monitor moisture content (MC) of wood and standing trees. The TDR was used to detect the electromagnetic wave propagation time of four tree species (Betula platyphylla, Tilia tuan, Picea asperata, and Fraxinus mandshurica) at different MCs. During the TDR test, effects of probe insertion depths on MC predictive accuracy were considered. The best results were obtained at an insertion depth of 8 cm. At the selective 8 cm insertion depth, a species-specific MC prediction model (0.94 ≤ R2 ≤ 0.98), a generalized model for the four species (R2 = 0.65), and a hybrid model for the species with similar densities (0.80 ≤ R2 ≤ 0.96) were constructed, respectively. Overall, the species-specific MC prediction model showed good predictive ability for both tree and wood disc samples, including that TDR can be used to detect wood and standing tree MC. If possible, the hybrid model can be used for species with similar density.