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BACKGROUND: The global incidence of liver diseases is rising, yet there remains a dearth of precise research models to mimic these diseases. The use of normothermic machine perfusion (NMP) to study diseased livers recovered from liver transplantation (LT) recipients presents a promising avenue. Accordingly, we have developed a machine perfusion system tailored specifically for the human whole diseased livers and present our experience from the NMP of diseased livers. METHODS: Six diseased livers recovered from LT recipients with different diagnoses were collected. The diseased livers were connected to the machine perfusion system that circulated tailored perfusate, providing oxygen and nutrients. The pressure and flow of the system were recorded, and blood gas analysis and laboratory tests of perfusate and bile were examined to analyze the function of the diseased livers. Liver tissues before and after NMP were collected for histological analysis. RESULTS: Experiments showed that the system maintained the diseased livers in a physiological state, ensuring stable hemodynamics and a suitable partial pressure of oxygen and carbon dioxide. The results of blood gas analysis and laboratory tests demonstrated a restoration and sustenance of metabolism with minimal damage. Notably, a majority of the diseased livers exhibited bile production continuously, signifying their vivid functional integrity. The pathological characteristics remained stable before and after NMP. CONCLUSION: We successfully established the machine perfusion system tailored specifically for diseased human whole livers. Through the application of this system, we have developed a novel in vitro model that faithfully recapitulates the main features of human liver disease. This model holds immense promise as an advanced disease modeling platform, offering profound insights into liver diseases and potential implications for research and therapeutic development.
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Some studies showed that the polymorphisms of vitamin D receptor (VDR) gene were associated with pulmonary diseases. However, the relationship between the VDR variations and susceptibility to coal worker's pneumoconiosis (CWP) remains unclear. The study aimed to determine the associations between VDR polymorphisms and susceptibility to CWP in Chinese Han population. The study involved 340 CWP patients and 312 healthy controls. The VDR polymorphisms were determined by DNA sequencing, and serum 25(OH)2D levels were detected by Ultra High-Performance Liquid Chromatography-Tandem Mass Spectrometry. The results showed that the VDR gene ApaI T allele increased the risk of CWP (OR = 1.486, 95% CI = 1.125-1.963, P = 0.006) and ApaI GT genotype as well as TT genotype increased the risk of CWP (GT vs. GG, OR = 1.461, 95% CI = 1.048-2.038, P = 0.025; TT vs. GG, OR = 2.673, 95% CI = 1.017-7.025, P = 0.039). Five haplotypes were identified and we found that the TGGT haplotype was associated with a lower risk of CWP (OR = 0.755, 95% CI = 0.603-0.946, P = 0.014). Meanwhile, multifactor dimensionality reduction analysis showed that the interaction between ApaI and exposure was the strongest, followed by TaqI and then BsmI. The study also found that the serum 25(OH)2D mean levels of the case group were significantly lower than that of the control group, and the serum 25(OH)2D mean levels of ApaI homozygous mutant and heterozygous mutant subjects were lower than that of the wild homozygosity, respectively (P < 0.001). The results suggested that ApaI T allele and GT or TT genotype and lower 25(OH)2D levels were increased the risk of CWP in Chinese Han population.
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Developmental dysplasia of the hip (DDH) is common, and features a widened Sharp's angle as observed on pelvic x-ray images. Determination of Sharp's angle, essential for clinical decisions, can overwhelm the workload of orthopedic surgeons. To aid diagnosis of DDH and reduce false negative diagnoses, a simple and cost-effective tool is proposed. The model was designed using artificial intelligence (AI), and evaluated for its ability to screen anteroposterior pelvic radiographs automatically, accurately, and efficiently.Orthotopic anterior pelvic x-ray images were retrospectively collected (n = 11574) from the PACS (Picture Archiving and Communication System) database at Second Hospital of Jilin University. The Mask regional convolutional neural network (R-CNN) model was utilized and finely modified to detect 4 key points that delineate Sharp's angle. Of these images, 11,473 were randomly selected, labeled, and used to train and validate the modified Mask R-CNN model. A test dataset comprised the remaining 101 images. Python-based utility software was applied to draw and calculate Sharp's angle automatically. The diagnoses of DDH obtained via the model or the traditional manual drawings of 3 orthopedic surgeons were compared, each based on the degree of Sharp's angle, and these were then evaluated relative to the final clinical diagnoses (based on medical history, symptoms, signs, x-ray films, and computed tomography images).Sharp's angles on the left and right measured via the AI model (40.07°â±â4.09° and 40.65°â±â4.21°), were statistically similar to that of the surgeons' (39.35°â±â6.74° and 39.82°â±â6.99°). The measurement time required by the AI model (1.11â±â0.00âs) was significantly less than that of the doctors (86.72â±â1.10, 93.26â±â1.12, and 87.34â±â0.80âs). The diagnostic sensitivity, specificity, and accuracy of the AI method for diagnosis of DDH were similar to that of the orthopedic surgeons; the diagnoses of both were moderately consistent with the final clinical diagnosis.The proposed AI model can automatically measure Sharp's angle with a performance similar to that of orthopedic surgeons, but requires far less time. The AI model may be a viable auxiliary to clinical diagnosis of DDH.