Abstract: Photoperiod is an important environmental factor that influence seasonal reproduction behavior in bird. Birds translates photoperiodic information into neuroendocrine signals through deep brain photoreceptors (DBPs). OPN5 has been considered as candidate DBPs involving in regulation of seasonal reproduction in birds. However, little is known about the effect of OPN5 in non-seasonal breeding birds. Thus, we pondered on whether OPN5 regulating follicular development through TSH-DIO2/DIO3 system responds to different photoperiods in non-seasonal laying ducks. As an ideal non-seasonal breeding bird, a total of 120 mountain ducks were randomly divided into three groups and treated respectively to a different photoperiod: group S (8 L:16D), group C (17 L:7D), and group L (24 L:0D). The ducks were caged in a fully enclosed shelter with the same feeding conditions for each group, free water and limited feeding (150 g per duck each day). Samples were collected from each group at d 0, d 5, d 8, d 20, and d 35 (n = 8). The ducks in 24 h photoperiod had the highest laying rate and the lowest feed-to-egg ratio, while the ducks in 8 h photoperiod had the lowest laying rate and the highest feed-to-egg ratio. Long-day photoperiod for 24 h significantly increased the ovarian index and GnRH, LH, E2, and P4 levels in serum; short-day photoperiod for 8 h increased testosterone levels in serum. Compared with 8 h photoperiod, long-day photoperiod significantly or highly significantly increased the mRNA level and protein expression of OPN5 in the hypothalamus of long-day photoperiod on d 35 (p < 0.05). The gene or protein expression patterns of GnRH, TRH, TSHβ, DIO2, THRβ, VIP, and PRL were positively correlated with OPN5, whereas the gene expression patterns of GnIH and DI O 3 were negatively correlated with OPN5. The results revealed that OPN5 mediated the effect of light on follicular development through the TSH-DIO2/DIO3 pathway, the expression of OPN5 increased with light duration and improved the efficiency of the HPG axis to promote follicular development in mountain ducks. Opsin 5(OPN5) is a novel G protein-coupled receptor (GPCR), firstly identified in mammalian neural tissue in 2003 , and maiReproduction in birds is regulated by the hypothalamic-pituitary-gonadal axis, and the HPG axis is a complete feedback system formed by the hypothalamus, pituitary, and gonads under central nervous regulation . Hypothavia small interfering RNA antisense in the MBH revealed that there is an inhibitory input in the photoinduced regulation of TSHβ mRNA expression  are seasonal breeding animals, and breeding status is divided into breeding and non-breeding seasons due to changes in sunlight throughout the year. However, domesticated poultry, such as chickens and ducks, can breed throughout the year . In bothpression . In the All experimental procedures in this study were performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals  and EU Directive 2010/63/EU for animal experiments. All animal experiments were approved by the Animal Care and Use Committee of Zhongkai University of Agriculture and Engineering .n = 5 or n = 8), five samples per group for qPCR, five or eight for hormone assays , five for western blots and three for immunohistochemistry, in which d 0, 5, 8, 20, and 35 were August 6, August 11, August 14, August 25, and 10 September 2020, respectively, China. The serum was separated and reserved for analysis of serum hormone levels, follicular development was examined, gonadal index was calculated, and hypothalamic and pituitary tissue samples were collected and reserved (−80°C) for tests of expression of related genes and proteins.The trial was undertaken at the Institute of Animal Health, Guangdong Academy of Agriculture Sciences, Guangzhou, Guangdong Province, China. One hundred and twenty normal laying mountain ducks were pre-fed for 15 d in 150-day-old with limited food (150 g per duck each day) and unlimited water at three constant rooms temperature of 21°C and light intensity of 400 lux under a 17 h light/7 h dark cycle. At 165-day-old of age, all ducks were randomly divided into three groups, with 40 birds in each group kept in cages, among them two ducks in a cage . They weµ IU/ml, coefficient of variation CV less than 8%; the detection range of LH is 0.5–100miu/ml, and the coefficient of variation CV is less than 8%.The determination of E2/P4/GnRH/T/PRL/LH in serum was performed according to the kit instructions. These kits were: Duck Estradiol (E2) ELISA KIT , Duck Progesterone (P4)/GnRH/Testosterone ELISA KIT , Duck Prolactin (PRL) ELISA KIT, and Chicken Luteinzing hormone (LH) ELISA KIT . Among them, the detection range of E2 is 40–1000 pg/ml, and the coefficient of variation CV is less than 10%; the detection range of P4 is 0.31–20 ng/ml, and the coefficient of variation CV is less than 10%; the detection range of GnRH is 15.63–1000 pg/ml, and the coefficient of variation CV is less than 10%; the detection range of T is 0.31–20 ng/ml, and the coefficient of variation CV is less than 10%; the detection range of PRL is 40–1000 TMRT reagent Kit with gDNA Eraser . Real-time quantitative polymerase chain reaction (qRT-PCR) was used to quantify the expression of OPN5, TSHβ, TRH, TSHR, THRB, DIO2, DIO3, GnRH, GnIH, PRL, GnRHR, GnIHR, FSH, and LH mRNA expression levels in hypothalamic or pituitary tissue. The qRT-PCR analysis was performed using the Applied Biosystems Quant Studio seven Flex Real-Time PCR System . Based on the reference sequence on NCBI, the fluorescent quantitative primers for the above genes and the primers for the β-actin gene as an internal reference were designed using Primer 5.0 . After denaturation by SDS , the protein was sampled using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) of 10–15%, and electrophoresis was performed at 80 V for 15 min and at 120 V for 60 min. Upon completion of electrophoresis, the protein was transferred to polyvinylidene fluoride (PVDF) membranes at 200 mA and after blocking with phosphate-buffered saline with Tween 20 (PBST) containing 5% fat-free milk, PVDF membranes were co-incubated with the antibodies, anti-β-actin , anti-OPN5 , anti-THRA/THRB (C3) , anti-GnRH-1-Ab , anti-GnIHand anti-DIO2 . They were then incubated overnight at 4°C and washed five times at TBST each for 3 min. The secondary antibodies, goat anti-rabbit  and goat anti-mouse , were incubated for 1 h at room temperature and washed three times at TBST for 10 min each. Proteins were detected using the ECL kit , and visualized using a Tanon-5200Multi device , photographed and stored. Densitometry analysis was performing using ImageJ software.Deparaffinizing and rehydrating the paraffin section: the sections were placed into xylene I for 15 min, xylene II for 15 min, xylene III for 15 min, absolute ethanol I for 5 min, absolute ethanol II for 5 min, 85% alcohol for 5 min, 75% alcohol for 5 min, and then rinsed in distilled water. Antigen retrieval: The tissue sections were placed in a repair box filled with citric acid (pH 6.0) antigen retrieval buffer for antigen retrieval, within a microwave oven, heated on medium power for 8 min until boiling, kept warm for 8 min, and then heated again, this time on medium-low power, for 7 min. During this process, excessive evaporation of buffer should be prevented and the sections should not be allowed to dry. To cool the sections to room temperature before proceeding, the sections were placed in PBS (pH 7.4) and shaken on the decolorization shaker three times for 5 min each. Blocking endogenous peroxidase activity: the sections were placed in 3% hydrogen peroxide and incubated at room temperature in darkness for 25 min. The sections were placed in PBS (pH 7.4) and shaken on a decolorizing shaper three times for 5 min each. Serum sealing: 3% BSA was added to the circle to evenly cover the tissue, and the tissues were sealed for 30 min at room temperature . Primary antibody incubation: the sealing solution was gently removed, the primary antibody prepared with PBS (pH 7.4) and then a known proportion was added to the sections and the sections placed flat in a wet box to be incubated overnight at 4°C . Secondary antibody incubation: the sections were placed in PBS (pH 7.4) and washed by shaking on the decolorizing shaker three times for 5 min each. After the sections were slightly dried, the tissues were covered with a secondary antibody (HRP labeled) from the corresponding species of primary antibody and incubated at room temperature for 50 min. DAB chromogenic reaction: the sections were placed in PBS (pH 7.4) and shaken on the decoloring shaker three times for 5 min each. DAB color developing solution, newly prepared, was added to the circle after the sections had slightly dried. The color developing time was controlled under the microscope, until the optimal color of brownish yellow was reached. Subsequently, the sections were rinsed with tap water to stop the reaction continuing. Nucleus counterstaining: the sections were counterstained with hematoxylin stain solution for about 3 min, washed with tap water, differentiated with hematoxylin differentiation solution for several seconds, then washed again with tap water, treated with hematoxylin returning blue solution; and washed once more with tap water. Dehydration and mounting: The sections were placed in 75% alcohol for 5 min, then 85% alcohol for 5 min, absolute ethanolⅠfor 5 min, anhydrous ethanol Ⅱfor 5 min, n-butanol for 5 min, and then xyleneⅠfor 5 min. Now dehydrated and transparent, they were then removed from the xylene and allowed to slightly dry, and then mounted with neutral gum. Staining of tissue was visualized using 3D Histech Quant Center 2.1 .The nucleus of hematoxylin stained is blue, and the positive expression of DAB is brownish yellow.p < 0.05. Graphics were plotted using the ggplot2 package in R.Statistical analyses were performed using Prism 7 . Multiple comparison analysis was performed using a two-way ANOVA followed by Tukey’s post-hoc correction for multiple comparisons. All experimental data were analyzed using the means or the means ± standard error of mean (S.E.M.). and differences were considered to be significant at p < 0.05), while group C was significantly higher than group L (p < 0.05). Ovarian development results (p < 0.05) and shortened light exposure significantly decreased the ovarian index (p < 0.05). There was no significant difference in the number of LYF (Large Yellow Follicle) in the three groups under different photoperiods. However, the number of SYF  and LWF (Large White Follicle) was highest in group L and significantly higher (p < 0.05) at d 8, d 20, and d 35 than in group C, and significantly higher (p < 0.05) in group C than in group S.The results showed no difference in laying rate, feed-to-egg ratio, and ovarian index in the three groups under the same light conditions (17 L:7D). After experiencing different light conditions, the average cumulative number of eggs laid per duck was 26.0, 22.0, and 19.6 in group L, C, and S, respectively. Prolonged light exposure increased laying performance and reduced light exposure suppressed laying performance, with the highest laying performance exhibited by group L . The tre results  showed tp > 0.05) in the GnRH, P4, LH, T, PRL, and E2 levels in serum under the same light condition  and the level of testosterone was significantly decreased (p < 0.05) as the duration of light exposure increased. In contrast, the levels of GnRH, P4, LH, E2, and PRL in serum were highly significantly inhibited (p < 0.05), while levels of testosterone were highly significantly increased (p < 0.05) after the short-day photoperiod treatment.Duck serum hormones showed no significant differences , and d 35 expression was reduced compared with d eight and d 20, but still higher than the control level (p > 0.05). These results were consistent with the results of western blots (p < 0.05).Detection of the expression pattern of hypothalamic OPN5 revealed  that OPNrn blots  and immurn blots . The expTRH, TSHβ, TSHR, DIO2, and THRβ in the pathway were basically consistent with the expression pattern of OPN5, which gradually increased under long-day photoperiod and decreased under short-day photoperiod. While the expression pattern of DI O 3 was inversely correlated with OPN5, DIO3 in group S was significantly higher than was DIO3 in group C (p < 0.05)  . Detecti < 0.05)  and the  < 0.05)  demonstrGnRH, GnRHR, FSH, LH, VIP, and PRL gradually increased in group L, and the relative gene expression of GnIH and GnIHR gradually decreased; the opposite of group S , TSHβ and PRL (r = 0.63), TRH and THRβ (r = 0.63), TSHβ and THRβ (r = 0.63), TSHβ and TRH (r = 0.67), VIP and TSHR (r = 0.7), and GnIH and DIO3 (r = 0.83). The top three most negatively correlated pairs of genes were TRH and PRL (r = -0.75), GnIH and THRβ (r = -0.75), and GnIH and TSHβ (r = -0.73). .At the protein level, OPN5 was positively correlated with GnRH, THR, DIO2, and was negatively correlated with GnIH. Western blot results showed that the three groups of proteins with the highest correlation were GnRH and GnIH (r = -0.85), OPN5 and THR (r = 0.69), and GnRH and THR (r = 0.61). .OPN5 is a deep brain photoreceptor that mediates the regulation of light on avian reproductive activity . To dateIn the present study, we observed that the morphology and function of the three groups of follicles were dramatically changed by altering the photoperiod. Compared to 17 h light time, 24 h light time showed higher egg production and lower egg ratio, while 8 h light time suppressed egg production and increased egg ratio in the mountain ducks. GnRH, P4, E2, LH and PRL levels in serum increased with longer light time, which increased the amount of LYF, SYF and LWF. Our results confirmed previous observations that male Japanese quail transferred to different photoperiods undergo a rapid change in plasma reproductive hormones and gonad weight . The resOPN5 mRNA expression and testicular development are promoted by prolonging light time in the Gallus  promoted the follicular development in the ovary of mountain ducks and improved egg-laying performance. Long-day photoperiod might have affected the expression of OPN5, TSH, DIO2, VIP and PRL, decreased the expression of DIO3, and promoted follicle development. While short-day photoperiod down-regulated the expression of OPN5, TSH, DIO2, VIP and PRL, elevated the expression of DIO3 and inhibited follicle development and egg production performance. The results suggested that OPN5 might affect the expression and secretion of reproduction-related genes in the gonadal axis through the TSH-DIO2/DIO3 pathway under different photoperiods, which in turn regulated follicular development and egg-laying performance in mountain ducks.


To explore changes in the epidemiological and clinical characteristics of patients who underwent knee arthroplasty (KA) over a 10-year period in China.Medical records of patients with knee osteoarthritis (KOA), who underwent primary unilateral KA in 5 level I center hospitals in China between January 2011 and December 2020, were retrospectively reviewed and analyzed. To more clearly define changes over the years, patients were divided into two groups according to time of admission at 5-year intervals. Age, sex, body mass index (BMI), Kellgren–Lawrence (K–L) classification, comorbid diseases, surgical procedures, hospital stay, and hospitalization costs were compared between the two groups.n = 7606 vs. group B, n = 16,004). Significant differences were noted in age, sex, BMI, K–L classification, comorbidities, surgical procedures, hospital stay, and hospitalization costs between the two periods (P < 0.05). More than three-quarters of KA cases involved females, and the age at surgery tended to be younger than that reported in foreign countries. In group B, the proportion of overweight and grade III, number of comorbidities, and unicompartmental knee arthroplasty patients increased compared to that in group A; however, hospitalization costs and length of hospital stay decreased.A total of 23,610 patients with KOA  who underwent primary unilateral KA were included. The number of KAs increased in recent years (group A, Results suggested that the epidemiological characteristics of patients undergoing KA have changed over time. An analysis of the epidemiological characteristics of patients undergoing KA treatment may provide a scientific basis for the prevention and control of KOA. Knee osteoarthritis (KOA) is a chronic and degenerative joint disease that occurs mostly in middle-aged and elderly individuals . Knee paKnee arthroplasty (KA), including total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA), is considered to be the best treatment for patients with end-stage KOA, with high safety and efficacy profiles . When deTo comprehensively understand the epidemiological characteristics of KA in China, information from patients who underwent primary unilateral KA due to KOA in five Level I center hospitals in China over the past 10 years was retrospectively collected. Trends in age, sex, body mass index (BMI), K–L classification, comorbidities, surgical procedures, length of hospital stay, and hospitalization costs were analyzed.The present study was conducted in accordance to the principles of the Declaration of Helsinki. The study protocol was approved by the ethics committee of the five participating hospitals. A retrospective analysis of 23,610 patients with KOA, who underwent KA at five Level I center hospitals in east, north, and central China between January 2011 and December 2020, was performed. Inclusion criteria were as follows: patients who underwent primary unilateral KA (TKA or UKA) due to KOA; and patients who provided informed written consent for the treatment plan and agreed to undergo postoperative follow-up. Individuals with rheumatoid arthritis or other inflammatory diseases; those who underwent previous KA, patients with physical, neurological, or mental conditions that could compromise compliance with postoperative rehabilitation and follow-up, and those with known sensitivity to the internal implant material used in the surgery were excluded.2; normal, 18.5 to 23.9 kg/m2; overweight, 24.0 to 27.9 kg/m2; and obese, ≥ 28 kg/m2.To evaluate clinical changes more clearly, patients were divided into two groups according to the time of admission at 5-year intervals: group A (2011–2015); and group B (2016–2020). After reviewing the medical records of all patients with KOA, clinical data were analyzed according to the number of inpatients admitted. Data extracted included age, sex, BMI, K–L classification, combined diseases, surgical procedures, hospital stay, and hospitalization costs. To further investigate age at high incidence, patients were divided into age groups spanning 10 years. BMI categories were defined as follows: underweight, ≤ 18.5 kg/mt test or Mann–Whitney U test, depending on whether the variables were normally distributed. Categorical variables are expressed as number and percentage (%) and were compared using the chi-squared test or Fisher’s exact test, as appropriate. Data that did not conform to a normal distribution expressed as median and interquartile range, and comparisons between groups were performed using the rank-sum test. Differences with P < 0.05 were considered to be statistically significant.All statistical analyses were performed using the SPSS version 26.0 . Continuous variables are expressed as mean and standard deviation (SD) and were compared using the independent samples Student’s 2 (range 14.6–57.8 kg/m2). Overweight individuals accounted for the highest proportions (42.1%), followed by obese patients (32.3%). According to the K–L classification, 22,128 (93.7%) patients were grade IV and 1482 (6.3%) patients were grade III. A total of 16,403 (69.5%) patients had a medical disease. Common comorbidities included hypertension, diabetes, coronary heart disease, and cerebrovascular disease. Classified according to the number of comorbidities, 8185 (34.7%) cases were combined with one medical disease, 4586 (19.4%) were combined with two medical diseases, and 3632 (15.4%) were combined with ≥ 3 medical diseases. The length of hospital stay of patients who underwent TKA ranged from 1 to 104 days, with a median of 11 days and an interquartile range of 8 days. Hospitalization costs ranged from $1336.20 to $40,048.10, with a median of $8393.90 and an interquartile range of $2460.00. The length of hospital stay of patients who underwent UKA ranged from 4 to 38 days, with a median of 9 days and an interquartile range of 6 days. Hospitalization costs ranged from $1525.20 to $20,122.80, with a median of $7319.40 and an interquartile range of $2662.60  pandemic . A significant difference was noted in sex between the two groups, with the number of male patients in group B significantly higher than that in group A (P = 0.001).Between 2011 and 2015, 7606 KA patients were enrolled, including 1641 males and 5965 females. The male-to-female ratio in this group was 1:3.63, with a mean age of 65.4 years (range 23–93 years). Between 2016 and 2020, 16,004 patients with KA were included, including 3759 males and 12,245 females. The male-to-female ratio in this group was 1:3.26, with a mean age of 65.9 years (range 20–92 years). Although the age of patients in group B did not increase significantly compared with group A, there was statistically significant difference . However, the difference in the male-to-female ratio in other age groups was not statistically significant ; among female patients, the proportion was 43.9% 2618/5965). In group B, the 60–69 years’ age group accounted for the largest number of patients. Among male patients, the proportion in this age group increased from 39.6% to 45.9% (1725/3759). Among female patients, the proportion in this age group increased to 48.9% . In addition, in the two periods, patients 50–59 years and 60–69 years of age, the proportion of female patients was significantly higher than that of male patients  , followed by normal-weight patients accounting for 27.9% (2122/7606). In group B, overweight and obesity accounted for 76.0% , followed by normal-weight patients, accounting for 23.0% . The proportion of overweight and obese patients in group B was significantly higher than that in group A. There was a statistically significant difference in the composition ratio of BMI between the two groups  , followed by patients with grade III accounting for 5.7% (437/7606). In group B, patients with grade IV accounted for 93.5% , followed by patients with grade III, accounting for 6.5% . The proportion of patients with grade III in group B was significantly higher than that in group A. There was a statistically significant difference in the composition ratio of K–L classification between the two groups (P < 0.001)  of patients had comorbidities, among whom 2787 36.7%) had one medical system comorbidity, 1409 (18.5%) had two, and 870 (11.4%) had ≥ 3. In group B, 70.8%  of patients had comorbidities, among whom 5398 (33.7%) had one medical system comorbidity, 3177 (19.8%) had 2, and 2762 (17.3%) had ≥ 3. Compared with group A, patients in group B had more comorbidities, and the difference was statistically significant (6.7% had P < 0.001)  patients underwent TKA and 359 (4.7%) underwent UKA. In group B, 15,042 (94.0%) patients underwent TKA and 962 (6.0%) underwent UKA. There was a statistically significant difference in the constituent ratio of the surgical procedures between the two groups (% patientP < 0.001)   reported that the mean age was 68 years  [Previous studies have analyzed that a number of scholars analyzed the epidemiological characteristics of KA. Chikud et al.  conducte9 years) . Namba e9 years)  conducte9 years)  analyzed9 years) . A compa2. Similarly, Belmont et al. [2). In the past 5 years, the proportion of overweight and obesity has significantly increased compared to the previous 5 years, which may be explained by improvements in living standards, caloric intake, exercise reduction, and sedentary lifestyles. At the same time, it has been reported that BMI ≥ 35 kg/m2 is a strong risk factor for exercise restriction after KA surgery; moreover, high BMI is closely associated with prolonged hospital stay and readmission rates [P = 0.02).Of note, obesity has been a well-established risk factor for KOA , 20. Witt et al.  searchedon rates –24. TherPresently, there are many related studies investigating the comorbidities of KA patients, mainly including hypertension, diabetes, cardiovascular and cerebrovascular diseases , 26. ChiResults of our study demonstrated that the median length of hospitalization for TKA and UKA patients was 11 and 9 days, respectively, and hospitalization days were significantly shorter than those in the recent period and the previous 5 years. The median hospitalization cost for TKA patients was $8393.90 and $7319.40 for UKA patients, and costs decreased between the most recent 5 years and the previous 5 years. Many investigators have performed large-sample studies investigating the length of hospital stay and hospitalization costs of KA patients. Bolognesi et al.  analyzedIt has been established that shortening the length of hospital stay is very important in reducing the economic burden and medical expenses of KA patients . A possiThe present study had several limitations, the first of which was its retrospective design, and inherent biases, which may have been unavoidable. Furthermore, because the patients who underwent KA treatment in the two groups were treated in different periods, changes in the medical environment and advances in surgical techniques may have affected effected clinical outcomes. Finally, we only conducted an epidemiological analysis of limited clinical data, some important data such as PSI and robotics alignment were not included. In future studies, more valuable variables need to be collected and analyzed.In summary, the changing trends of the epidemiological characteristics of patients who underwent primary unilateral KA were more than three-quarters of KA occurred among females, and the age at surgery tended to be younger than that in foreign countries. The proportion of overweight and patients undergoing UKA surgery has increased. Moreover, compared with 2011 to 2016, the number of patients undergoing KA treatment from 2016 to 2020 complicated with medical diseases increased significantly; however, hospitalization costs and hospital stays showed a downward trend. This study has shed light on the epidemiological characteristics of KA patients in eastern, northern, and central China between 2011 and 2020 and may prove helpful for the prevention of KOA and/or control of its progression.


THC and CBN were isolated, and their identities were confirmed by comparing 1H- and 13C-NMR spectra and mass spectra with a database. Cell proliferation, cell migration, and cell apoptosis assays were performed in HuCCT1 human CCA cells treated with THC or CBN. The phosphorylation of signaling molecules in HuCCT1 cells was detected. To determine the effects of THC and CBN in an animal model, HuCCT1 cells were inoculated subcutaneously into nude mice. After the tumors reached an appropriate size, the mice were treated with THC or CBN for 21 days. Tumor volumes were monitored and calculated. The 1H- and 13C-NMR data of THC and CBN were almost identical to those reported in the literature. THC and CBN significantly inhibited cell proliferation and migration and induced apoptosis in HuCCT1 cells. The phosphorylation of AKT, GSK-3α/β, and ERK1/2 decreased in HuCCT1 cells treated with THC or CBN. CCA xenograft mice treated with THC showed significantly slower tumor progression and smaller tumor volumes than control mice. THC and CBN induced apoptosis in CCA by inhibiting the AKT and MAPK pathways. These findings provide a strong rationale for THC and CBN as therapeutic options for CCA.Cholangiocarcinoma (CCA) is a very aggressive tumor. The development of a new therapeutic drug for CCA is required. This study aims to evaluate the antitumor effect of ∆ Cholangiocarcinoma (CCA) is an adenocarcinoma of the epithelial cells lining the intrahepatic and extrahepatic bile ducts . It is aCholangiocarcinogenesis is composed of a complex relationship of extracellular ligands, upregulated cell surface receptors, and dysregulated intracellular protein phosphorylation signaling pathways, leading to cell proliferation, survival, and metastasis . Chronic9-Tetrahydrocannabinol (THC) is the principal component of marijuana (Cannabis sativa). Currently, THC is used medically to treat chemotherapy-induced nausea and vomiting in cancer patients and anorexia in AIDS (acquired immune deficiency syndrome) patients ), cleaved PARP (Asp214), caspase 3, cleaved caspase 3 (Asp175), and β-actin were obtained from Cell Signaling Technology . LY 294002, U0126, radioimmunoprecipitation assay (RIPA) buffer and protease inhibitor cocktail were purchased from Cell Signaling Technology . Culture plates and flasks were obtained from Falcon . Matrigel basement membrane matrix was purchased from Corning . Tween 80 was purchased from P. C. Drug Center .RPMI 1640 medium and fetal bovine serum were purchased from Gibco . Polyclonal antibodies against protein kinase B (AKT) (phosphorylated at Ser473), glycogen synthase kinase 3 alpha/beta  was grown in RPMI 1640 medium supplemented with 10% fetal bovine serum at 37°C in a 5% CO1H- and 13C-NMR data with those in the literature and matching their mass spectral data to those in the NIST database. The purity was quantitatively analyzed by HPLC  (mobile phase: (ammonium formate: acetonitrile), gradient elution, flow rate 1.0 mL/min, column: XBridge C18, detection at 228 nm).THC and CBN were isolated in-house at the College of Pharmacy from dried cannabis samples by column chromatography and preparative HPLC. The identity of the compounds was confirmed by comparing their μM–100 µM, LY294002 [a selective inhibitor of phosphatidylinositol 3 (PI3) kinase], U0126 (a selective inhibitor of ERK1/2) at concentrations of 10 μM and 20 μM, or vehicle. After the treated cells were incubated for 24 h or 48 h, 50 mL of MTT [-2-5-diphenyltetrazolium bromide]  was applied in each well at a concentration of 0.5 mg/mL. The reaction was detected at 590 nm using a Benchmark Plus microplate reader .HuCCT1 cells were seeded in 96-well plates at a density of 10,000 cells/well. After 24 h of incubation, cells were treated with either THC or CBN at concentrations of 10 5 cells/well) and cultured for 24 h to form a confluent monolayer. A sterile 10 μL micropipette tip was used to create a straight-edged cell-free zone across the cell monolayer in each well. Then, the monolayer was washed with 500 μL PBS to remove the detached cells. Cells were treated with 15 μM of THC or CBN for 6 h–12 h. The scratch closure was imaged under phase contrast microscopy  at 4x magnification. The distance of wound width was analyzed using ImageJ digital imaging processing software . The percentage of cell migration was calculated as the percentage of wound closure: % wound closure = (A0h–A∆h)  × 100/A0h, where A0h is the area of the wound measured immediately after scratching and A∆h is the area of the wound measured h hours after the scratch is performed. Data of three replicates are presented as mean ± SD.HuCCT1 cells were seeded in 24-well plates  was used following the manufacturer's protocol. Adherent cells were collected, resuspended in 200 μL of binding buffer containing 5 μL annexin V : FITC and incubated for 10 min in the dark at room temperature. Cells were then washed, resuspended in 200 μL of binding buffer containing 10 μL of propidium iodide solution, and analyzed by flow cytometry .Cell apoptosis was determined by flow cytometric analysis. HuCCT1 cells were seeded in culture flasks. After 18 h of incubation, cells were treated with THC or CBN at concentrations of 10 The numbers of early and late apoptotic cells are shown in the lower right quadrant and upper right quadrant of the histograms, respectively. The lower left quadrants indicate living cells.7 HuCCT1 cells were seeded in culture flasks overnight. Cells were then treated with THC or CBN (at concentrations of 20 μM) or a vehicle. After 18 h of incubation, the cells were analyzed by a Proteome Profiler Array Human Phosphokinase Array Kit  following the manufacturer's instructions. Briefly, cell lysate was incubated overnight with the human phosphokinase array membrane. The membrane was washed to remove unbound proteins and then incubated with a cocktail of biotinylated detection antibodies. Streptavidin-HRP and chemiluminescent detection reagents were applied. The chemiluminescent signal was determined by UVP ChemStudio  and analyzed by ImageJ digital imaging processing software.To detect signaling pathways, 1 × 105 cells/well, then treated with THC or CBN (at concentrations of 10 μM and 20 μM) or a vehicle for 18 h. Treated cells were collected, washed with PBS, and lysed in RIPA buffer containing a 1% protease inhibitor cocktail. Western blot analyses were performed as previously reported [α/β, ERK1/2, PARP (46D11), cleaved PARP, and β-actin. Colorimetric detection of antigen-antibody complexes revealed the target proteins on the membrane using an Opti-4CN Detection Kit  following the manufacturer's instructions. Semiquantification of the protein bands was analyzed using the ImageJ digital imaging processing software. The expression of each analyzed protein was normalized with β-actin. The samples were analyzed three times in independent blots.HuCCT1 cells were seeded in a six-well culture plate at a density of 5 × 10reported . The blo6 HuCCT1 cells in 100 μL serum-free RPMI 1640 medium and 100 μL matrigel was inoculated subcutaneously into the right flank of each mouse. After tumors reached an average volume of 150 mm3, mice were then randomized into five treatment groups of seven animals each: 1% Tween 80 (control); 15 mg/kg THC; 30 mg/kg THC; 20 mg/kg CBN; and 40 mg/kg CBN. THC and CBN injections were prepared in a water base to provide good syringeability and injectability and avoid tissue damage from the hyperosmolarity of the formulation. For the control, 1% Tween 80 was used. The subcutaneous injection treatment was repeated every morning for 21 d (L × W × W)/2, where L is the long diameter of the tumor and W is the short diameter of the tumor. The body weights of the mice were recorded twice a week. Tumors were maintained in 10% buffered formalin for histopathological and immunohistochemical examination.Athymic nude (BALB/cAJcl-Nu/Nu) mice were obtained from Nomura Siam International Co., Ltd. . The animal protocol was designed to minimize pain and discomfort for the animals. Ethical approval for the study was obtained from Research Institute, Rangsit University . Four-week-old female mice (weighing 12.7 g–17.0 g) were housed and acclimatized to laboratory conditions  for one week prior to experimentation. All animals had free access to sterile food and water and were cared for under specific pathogen-free conditions. A mixture of 3 × 10for 21 d . Tumor vThe specimen sections were deparaffinized and rehydrated. Then, endogenous peroxidase was blocked using a hydrogen peroxide block . The specimens were washed, and antigen retrieval was performed by heating in a 10 mM citrate buffer. After protein block solution  was applied to reduce nonspecific background staining, the specimens were incubated with primary antibodies overnight at room temperature in a humidified chamber. The antigen-antibody complex was then detected using a Mouse and Rabbit Specific HRP/DAB (ABC) Detection IHC Kit , following the manufacturer's instructions. The sections were counterstained with hematoxylin solution  and dehydrated before mounting with mounting medium . As a negative control, the primary antibody was replaced with SignalStain antibody diluent . The stained specimens were examined under a Motic BA210 microscope.U test was used for comparing two groups, and P < 0.05 was considered statistically significant. The rate of xenograft tumor progression (assessed by the number of mice with tumor volumes <400 mm3) was evaluated using survival curves and compared using the log-rank test. The sample size was determined using the G∗ power 3.1.9.7 program to detect a difference of 100 mm3 of tumor volume between the THC, CBN, and control groups of mice. Using an ANOVA test at a significance level of 0.05 and a power of 0.80, a sample size of seven from each group of mice was selected for the study. The efficacy of tumor induction was estimated to be 90%. Therefore, 40 mice were used in the experiment.The data are described as means, with standard deviations or percentages when appropriate. Data from three or more groups were compared using one-way ANOVA or nonparametric Kruskal–Wallis tests. The Mann–Whitney m/z 314 obtained from the GC-MS spectrum  δ 6.31 , 6.27 , 6.14 , 4.92 , 3.20 , 2.43 , 2.17 , 1.91 , 1.71 , 1.68 , 1.55 , 1.41 , 1.40 , 1.30 , 1.09 , and 0.88 ; 13C-NMR  δ 154.7 , 154.2 , 142.8 , 134.3 , 123.8 , 110.0 , 109.0 , 107.6 , 77.2 , 45.8 , 35.5 , 33.6 , 31.5 , 31.2 , 30.6 , 27.5 , 25.0 , 23.3 , 22.5 , 19.2 , and 14.0 .THC  was isolspectrum . Its 1H-terature . The datm/z 310 in the GC-MS spectrum  δ 8.17 , 7.15 , 7.07 , 6.44 , 6.29 , 5.25 , 2.50 , 2.39 , 1.62 , 1.61 , 1.33 , and 0.90 ; 13C-NMR  δ 154.6 , 153.0 , 144.5 ,136.9 , 127.6 , 126.4 , 122.6 , 110.8 , 109.9 , 108.7 , 77.3 , 35.6 , 31.5 , 30.4 , 27.1 , 22.5 , 21.5 , and 14.0 .CBN  was isolspectrum . Its 1H-terature . The datBased on the HPLC chromatograms detected at 228 nm, the compound purities for THC and CBN were found to be 99.31% and 99.76%, respectively.μM to 100 μM or a vehicle (DMSO). THC and CBN concentrations of 10 μM had no significant effect on the inhibition of CCA cell proliferation compared with vehicle-treated cells. However, at high concentrations of THC and CBN (20 μM–100 μM), CCA cell proliferation was significantly inhibited in a dose-dependent manner  μM and 13.52 ± 1.04  μM, respectively. At 48 h of culture, they were 11.68 ± 2.86  μM and 10.98 ± 2.02  μM, respectively.Cell proliferation assays were performed in the HuCCT1 cells treated with THC or CBN at concentrations from 10 t manner . The IC5μM of THC and CBN were used because higher doses resulted in massive cell death. The percentage of HuCCA1 cell migration in the control group was 40.18% ± 6.74% at 6 h and 55.21% ± 5.81% at 12 h. Migrations of HuCCT1 cells treated with THC and CBN were significantly decreased at 6 h  and 12 h  , while the proportion increased from 25.43 ± 2.63% to 38.79 ± 4.28% with CBN concentrations of 10 μM–20 μM    or ERK1/2 (U0126). The results of the cell proliferation study demonstrated that inhibition of AKT or ERK1/2 with a specific inhibitor significantly decreased the rate of CCA cell proliferation (M U0126) .3 under the skin at the implantation site. THC, CBN, or control was injected around the tumor edge daily for three weeks. Nude mice in each group ate, drank, and excreted normally during the experiment. In addition, there was no significant difference between the average body weights of nude mice in the groups . Meanwhile, nude mice treated with 20 mg/kg CBN and 40 mg/kg CBN exhibited decreased tumor volumes, but the difference was not statistically significant from the control group (P = 0.230 for 20 mg/kg CBN and P=0.144 for 40 mg/kg CBN on day 21)  than the control group (P=0.009 for 15 mg/kg THC and P=0.001 for 30 mg/kg THC) (P=1.000 for 20 mg/kg CBN and P=0.056 for 40 mg/kg CBN). This finding suggests that THC had an inhibitory effect on tumor growth in vivo.From day 17 to day 21 of the interventional experiment, the tumor volume of the 30 mg/kg THC group was significantly smaller than that of the control group: on day 17, the average tumor volume in the 30 mg/kg THC group was 339.59 ± 65.26 mm Figures . On day /kg THC) . Tumor pTumor tissue sections revealed dense tubular and solid nests of adenocarcinoma that were well-circumscribed and encapsulated and that partially invaded the muscular layer, along with fine collagen fibers. The tumor cells were large and cuboidal to polyhedral, with light basophilic or amphophilic cytoplasm and large pleomorphic round to ovoid nuclei and one or two prominent nucleoli.H&E staining showed multifocal intralesional cell necrosis. Mainly pyknotic and karyorrhectic nuclei were found in 10% of the specimens from the 15 mg/kg THC-treated group, 20% of the specimens from the 30 mg/kg THC-treated group, and 10% of the specimens from the 40 mg/kg CBN-treated group. No evidence of cell necrosis was found in tumor specimens of the control group or the 20 mg/kg CBN-treated group . ImmunohAdditionally, the phosphorylation of AKT and ERK1/2 was found to be significant in the tumor specimens derived from the control group. Meanwhile, the phosphorylation of AKT and ERK1/2 was dramatically decreased in tumor specimens of the THC- and CBN-treated groups .To date, despite increased knowledge of CCA pathophysiology, only minor improvements in the treatment of this disease have been achieved. Therefore, identifying a novel treatment for CCA is important. THC and CBN share similar structural formulas. THC is the major psychoactive cannabinoid and shows various biological activities, including anticancer activity, in vitro and in vivo. CBN is found in trace amounts in the cannabis plant. CBN is mildly psychoactive, has anticonvulsant, sedative, anti-inflammatory, and antibiotic activities and promotes bone formation . TherefoμM. This finding is consistent with previous studies in which THC-induced apoptosis in many kinds of cancer cells [In this study, we demonstrate that both THC and CBN exhibited potent anticancer activity toward CCA in both cell culture and cancer xenograft mouse models. Both THC and CBN significantly inhibited the viability of CCA cells. Additionally, flow cytometric analysis using annexin V/PI showed that THC and CBN dose-dependently induced apoptosis in CCA cells. The effect of the cannabinoids on apoptosis in CCA cells was confirmed by western blot analysis. Cleavage of PARP 1 at Asp214 by caspase 3 is a useful marker of cell apoptosis. In this study, cleaved PARP was found in CCA cells treated with THC and CBN at a concentration of 20 er cells . Interesα/β at Ser21/9 decreased in HuCCT1 cells treated with THC. GSK-3α/β is a serine/threonine protein kinase that phosphorylates and inactivates glycogen synthase and regulates cyclin D1 proteolysis and subcellular localization. GSK-3α/β is a critical downstream signal of the AKT pathway [α/β activity can be inhibited by AKT-mediated phosphorylation at Ser21 of GSK-3α and Ser9 of GSK-3β [Our experiments showed decreased phosphorylation of AKT in HuCCT1 cells treated with THC and CBN. Our previous work demonstrated that signal transduction of AKT is a crucial mechanism of CCA cell proliferation and invasion . It has  pathway , 28. GSKf GSK-3β . TherefoOur previous study demonstrated that ERK1/2 signaling promotes CCA cell proliferation and that the inhibition of ERK1/2 contributes to cell apoptosis . The phoIn addition, we discovered that cell migration was significantly reduced in CCA cells treated with THC or CBN. Previous research has shown that THC-inhibited cancer cell migration is dependent on the AKT and MEK signaling pathways . As a reTo further extend the potential clinical applications, we tested the antitumor effect of THC and CBN on human CCA xenograft nude mice. The tumor volumes of mice treated with THC were significantly decreased compared to that of control mice. Although the in vitro study found that CBN distinctly inhibited CCA cells, the in vivo study demonstrated that the tumor volumes of mice treated with CBN were decreased but were not significantly different from that of the control group. However, an immunohistochemical study of tumor specimens showed that both THC and CBN induced cancer cell necrosis and apoptosis. Tumors from THC- and CBN-treated xenograft mice showed a reduction in phosphorylation of AKT and ERK1/2 in immunohistochemistry studies. This finding was associated with increases in cleaved PARP and cleaved caspase, both of which are markers of apoptosis.This study demonstrates that while CBN showed anticancer activity in xenograft mice, it showed less anticancer activity than THC. CBN may have weaker affinity and specificity with CB1, CB2, and TRPV2 receptors than THC, resulting in distinct pharmacological effects , 18, 33.These findings suggest that CBN has a weaker inhibitory effect on CCA xenograft mice than THC. Therefore, CBN could be synergistically applied with THC to inhibit CCA and prevent THC's psychotropic side effects. Further studies should address this possibility.THC and CBN induced apoptosis in CCA by inhibiting the AKT and MAPK pathways, leading to a decrease in cell proliferation in vitro and tumor volume in vivo. In addition, in this animal model, THC appeared to be superior in potency to CBN. These findings provide a strong rationale for THC and CBN as therapeutic options for CCA.


Patients with a continuous glucose monitor (CGM), higher education level, higher annual income, and older age were associated with lower A1C (p ≤ 0.001). Higher BMI and self-description as African American/Black were associated with higher A1C (p ≤ 0.01). Insulin pump use was associated with lower A1C (p < 0.01) in 2015–2016. Patients who used adjuvant medications did not demonstrate significant improvement in disease control. These data suggest that findings from well-designed research studies may not be consistently reproducible in real-world settings, due to patient-specific factors.Randomized controlled trials have demonstrated that noninsulin medications used to treat type 2 diabetes can improve health outcomes among patients with type 1 diabetes (T1D). This study assessed the effects of adjuvant diabetes medications on glycated hemoglobin (A1C), body mass index (BMI), or total daily insulin (TDI) among patients with T1D in a real-world setting. This was an analysis of the T1D Exchange Clinic Registry, using the study periods of 2010–2012, 2015–2016, and 2016–2017, to assess differences in A1C, BMI, and TDI between patients with and without adjuvant medications. The relationships between characteristics and A1C in 2015–2016 and 2016–2017 were determined. Analysis included 517 patients in the adjuvant medication cohort and 4968 in the insulin-only cohort. No significant improvement in A1C was observed. A significant difference in BMI and TDI between the insulin-only  and adjuvant medication cohorts  ( Type 1 diabetes (T1D) is an autoimmune disease that leads to the elimination of pancreatic beta cells, resulting in complete insulin deficiency . T1D is Further complicating the treatment of patients with T1D is the high prevalence of obesity . ObesityIn addition to pramlintide, numerous other medications commonly used for the treatment of T2D are used in patients with T1D . Also knThis was a retrospective observational cohort analysis of de-identified datasets obtained from the T1D Exchange Clinic Registry. The T1D Exchange Clinic Registry collected information from participants from 67 endocrinology centers in the United Sates (US) who elected to enroll and complete a questionnaire with demographic information and medical history. Data were obtained from medical records of patients. Patients who qualified for follow-up studies were identified and contacted using the information provided during initial screening. All T1D Exchange Clinic Registry study materials had been reviewed and approved by JAEB IRB . The Ohio State University Institutional Review Board determined that this study was exempt from review.Investigators obtained three datasets for the following clinical exam dates: 1 September 2010 to 1 August 2012 (2010–2012 study period), 30 April 2015 to 31 July 2016 (2015–2016 study period), and 1 May 2016 to 31 July 2017 (2016–2017 study period). A patient identification number was utilized to link relevant data pertaining to each patient across the datasets. Initially, all patients were included. Patients without a medication record or self-reported as pregnant in the 2015–2016 and 2016–2017 datasets were excluded. Patients in the 2015–2016 study period who were 18 years of age or younger or 75 years of age or older were also excluded. Medications were assessed in the 2015–2016 and 2016–2017 datasets. Patients without use of a medication to treat type 2 diabetes in the 2015–2016 and 2016–2017 datasets were identified as insulin-only users. Patients with insulin and additional medications to treat type 2 diabetes in the 2015–2016 and 2016–2017 study periods were identified as adjuvant medication users. Medications included in the analysis were metformin, SGLT2 inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, pramlintide, sulfonylureas, bile acid sequestrants, meglitinides, bromocriptine, and colesevelam, due to being used in >10 patients.Data were coded and organized using Microsoft Excel  and IBM SPSS software . Counts, percentages, medians and interquartile ranges were used to describe the population. Demographic data for patients was collected from the 2016–2017 dataset. Chi-squared tests and sign tests were used to assess differences between the insulin-only and the adjuvant medication cohorts, using the 2016–2017 study period. To assess if the use of adjuvant medications was associated with improvement in BMI or A1C, these outcome variables were collected from the 2010–2012, 2015–2016, and 2016–2017 study periods. The TDI outcome variable was only collected during the 2015–2016 and 2016–2017 datasets, due to differences in how data was recorded in the 2010–2012 study period.A two-way mixed repeated measures ANOVA assessed the differences in A1C and BMI, between the insulin-only and the adjuvant medication cohorts, using the 2010–2012, 2015–2016, 2016–2017 study periods. TDI values were compared between the 2015–2016 and 2016–2017 study periods. A1C and BMI were transformed using natural log. Outliers were defined as having a studentized residual less than −3 or greater than 3. Q-Q plots determined the studentized residuals for normality. A sensitivity analysis was performed, omitting outliers. Mauchly’s test for sphericity was violated during the execution of the mixed ANOVA analyses. Thus, within and between subjects’ effects were adjusted using the Huynh–Feldt correction. Given the assumption for Box’s test of equality of variance was violated and within subject interactions were detected, Friedman tests were also used to assess for differences in A1C and BMI between 2010–2012, 2015–2016, and 2016–2017 separately, among insulin plus adjuvant medication users versus the insulin-only cohort. Sign tests assessed differences in TDI between the 2015–2016 and 2016–2017 study periods in each cohort individually.p values. Variables included in the regression were self-reported age, BMI, Black/African American, self-reported as other ethnicity, gender, education level, income level, use of adjuvant medication, use of CGM, and use of an insulin pump. A two-tailed, a priori alpha level of 0.05 defined statistical significance. Bonferroni corrections were made for post-hoc testing of the Friedman test.Linear regression assessed the relationship between unique patient characteristics with A1C in the 2015–2016, and 2016–2017 study periods. The A1C was transformed using natural log for the linear regression analysis. BMI was converted to categories for the regression analysis [2 . Patients had a median age of 44 years and used a median of 45 insulin units per day.A total of 33,666 unique patients were initially considered for inclusion into the study. After the application of exclusion criteria, there were 517 patients in the adjuvant medication cohort and 4968 patients in the insulin-only cohort. The majority of patients included for analysis were female , did not smoke , did not use CGM devices , did not use an insulin pump , reported earning more than USD 50,000 per year , self-described as White non-Hispanic , received a degree from college , had an A1C greater than 7.5%, , and a BMI greater than 25kg/mAmong patients in the adjuvant medication cohort, there were 233 on metformin, 107 on SGLT2 inhibitors, six on DPP4 inhibitors, 117 on GLP1 agonists, eight on thiazolidinediones, four on sulfonylureas, 79 on pramlintide, 30 on a bile acid sequestrant, and none on meglitinides or bromocriptine. In this cohort there were 53 patients on two adjuvant medications, four patients on three adjuvant medications, and two individuals on four adjuvant medications.p < 0.001), less likely to smoke (p < 0.003), more likely to use a CGM device (p < 0.002), older (p < 0.001), with a higher BMI (p < 0.001), and utilizing more insulin units per day (p < 0.001), when compared to patients who only used insulin . Mauchly’s test for sphericity indicated that the assumption of sphericity was violated for the two-way interaction, x2 (2) = 829.149, p < 0.001. There was no statistically significant interaction between time and use of adjuvant medications, F  = 3.276, p = 0.17, partial η2 < 0.001. There was a statistically significant difference in mean A1C at the different date ranges, F  = 3.276, p = 0.046, partial η2 = 0.001. There was no statistically significant difference in mean A1C between the adjuvant medications versus insulin-only group F  = 0.755, p = 0.39, partial η2 < 0.001. This analysis included 3615 patients in the insulin-only cohort and 369 patients in the adjuvant medication cohort, as these patients had a reported A1C in all 3 datasets. There were 41 outliers in 2010–2012, 46 outliers in 2015–2016, and 45 in 2016–2017. There was slight deviation of normality at the tail end of the Q-Q plots of the studentized residuals. There was no homogeneity of covariances as assessed by Box’s test of equality of covariance matrices (p = 0.069). Mauchly’s test for sphericity indicated that the assumption of sphericity was violated for the two-way interaction, x2 (2) = 692.505, p < 0.001. There was no statistically significant interaction between time and the use of adjuvant medications, F  = 1.947, p = 0.15, partial η2 = 0.001. There was no statistically significant difference in mean A1C at the different study periods, F  = 2.866, p = 0.07, partial η2 = 0.001. There was a statistically significant difference in mean A1C between the use of adjuvant medications and the insulin-only group F  = 3.983, p = 0.046, partial η2 = 0.001. This analysis included 3520 patients in the insulin-only cohort and 366 patients with adjuvant medication use after omitting outliers. Q-Q plots of the studentized residuals reflected normality. Homogeneity of covariances was confirmed, as assessed by Box’s test of equality of covariance matrices (2 (2) = 14.456, p = 0.001) and the adjuvant medication cohort (x2 (2) = 8.229, p = 0.016). Sign tests with Bonferroni corrections were used for post-hoc comparisons. In the insulin-only cohort, there was a statistically significant difference in A1C between 2010–2012 (median = 7.6%) and 2016–2017 . In the adjuvant medication cohort, there was a statistically significant difference in A1C between 2015–2016 (median = 7.6%) and 2016–2017 . Individual assessments of each adjuvant medication cohort found no statistically significant difference in A1C at any time point . Mauchly’s test for sphericity indicated that the assumption of sphericity was violated for the two-way interaction, x2 (2) = 771.476, p < 0.001. There was a statistically significant interaction between time and the use of adjuvant medications F  = 3.821, p = 0.031 partial η2 = 0.001. There was a statistically significant difference in BMI at different study periods F  = 47.097, p < 0.001, partial η2 = 0.017. There was a statistically significant difference in mean BMI between the adjuvant medication cohort and the insulin-only cohort F  = 155.396, p < 0.001, partial η2 = 0.053. This analysis included 2519 patients in the insulin-only cohort and 250 patients with adjuvant medication, use as these patients had BMI reported at all three time points. In this sample, there were 17 outliers in 2010–2012, 20 outliers in 2015–2016, and 19 outliers in 2016–2017. Q-Q plots of the studentized residuals showed slight deviation from normality at the tail ends. There was no homogeneity of covariances, as assessed by Box’s test of equality of covariance matrices (p < 0.001). There was a statistically significant interaction between study periods and use of adjuvant medications F  = 3.901, p = 0.03, partial η2 =0.001. There was also a statistically significant difference in BMI at the different study periods, F  = 57.701, p < 0.001, partial η2 =0.021. A statistically significant difference was observed in BMI between the adjuvant medication and insulin-only cohorts F  = 184.042, p < 0.001, partial η2 = 0.063. After omitting outliers, there were 2484 patients in the insulin-only group and 247 patients in the adjuvant medication cohort. Q-Q plots of the studentized residuals reflected normality. There was no homogeneity of covariances, as assessed by Box’s test of equality of covariance matrices (2 (2) = 367.659, p < 0.001) and the adjuvant medication cohort (x2 (2) = 8.504, p = 0.014). Sign tests with Bonferroni corrections were used for post-hoc comparisons. In the insulin-only cohort, there was a statistically significant difference in BMI between 2010–2012 (median = 25.5 kg/m2), 2015–2016 (median = 26.2 kg/m2), and 2016–2017. In the adjuvant medication cohort, there was a statistically significant difference in BMI between 2010–2012 (median = 29.8 kg/m2) and 2015–2016  and between 2015–2016 (median = 30.5 kg/m2) and 2016–2017   .p = 0.150). There was no statistically significant interaction between the use of adjuvant medications and time on TDI, F  = 0.421, p = 0.52. There was no significant effect of time on TDI F  = 3.127, p = 0.08, partial η2 = 0.001. There was a statistically significant difference in average TDI between the adjuvant medication and the insulin-only group, F  = 24.702, p < 0.001, partial η2 = 0.008. This analysis included 2681 patients in the insulin-only cohort and 287 patients in the adjuvant medication cohort. There were 21 TDI outliers identified in 2015–2016 and 24 TDI outlier values in 2016–2017. Deviation from normality was identified at the tail end of Q-Q plots. Homogeneity of covariances was confirmed by Box’s test of equality of covariance matrices (p = 0.179). There was no statistically significant interaction between the use of adjuvant medications and time on TDI, F  = 0.744, p = 0.39, partial η2 < 0.001. There was a statistically significant effect of time on TDI between 2015 and2017 F  = 4.195, p = 0.04, partial η2 = 0.001. There was a statistically significant difference in average TDI between the adjuvant medication and the insulin-only group, F  = 28.933, p < 0.001, partial η2 = 0.010. This analysis included 2655 patients in the insulin-only cohort and 283 with adjuvant medication use. Q-Q plots of the studentized residuals reflected normality (p < 0.001). No significant difference between 2015–2016 and 2016–2017 was found in the adjuvant medication cohort (p = 0.95)  and 2016–2017  .p < 0.001, R2 = 0.12) and 2016–2017  = 54.581, p < 0.001, R2 = 0.13) each significantly predicted A1C. The use of a CGM, higher education level, higher annual income, and older age had a statistically significant association with lower A1C (p ≤ 0.001) in both datasets. Self-description as African American/Black and higher BMI had a direct significant relationship with higher A1C in both datasets (p ≤ 0.01). Use of an insulin pump was associated with lower A1C (p < 0.01), in the 2015–2016 dataset. This relationship was not observed in the 2016–2017 study period. Regression coefficients can be found in Two multivariate regression models were used to determine the relationship between unique patient characteristics with A1C using data from the 2015–2016 and 2016–2017 study periods. Regressions using data from 2015–2016  = 46.156, The effective management of glucose levels among patients with T1D remains a significant challenge. A previous study found that approximately one in five adults with T1D achieve A1C levels below 7% . A varieIn this study, patients with adjuvant medication use presented with no significant difference in A1C between 2010 and 2017, when compared to an insulin-only cohort. Additionally, the data suggested that A1C modestly increased over time among both adjuvant medication users and the insulin-only cohort. BMI significantly increased over time in both the adjuvant medication and control cohorts in this time period. TDI increased significantly in the insulin-only cohort from 2015 to 2017. It is important to note no significant difference in A1C, BMI, and TDI values were observed when adjuvant medication cohorts were examined for individual drugs, except for the metformin cohort that was associated with a significant increase in BMI.The findings from this study may differ from those reported in randomized controlled clinical trials for medication classes such as SLGT-2 inhibitors, amylin analogs, GLP-1 receptor analogs, and DPP4 inhibitors, which have demonstrated clinically significant and positive results on reducing body weight, total daily insulin needs, and A1C among patients with type 1 diabetes ,9,10,11.Finally, given that the brand medications were used off-label in this study, medication adherence may have been compromised due to various factors including costs, variations in insurance coverage, and health literacy. As an example, one study found that 17% of patients with diabetes were considered non-adherent to their medications, and this increased with the presence of insulin prescriptions and obesity . ClinicaThe findings from this study importantly emphasize targeted disease management approaches. Older age, higher annual income, the use of CGM, and higher education level were significantly associated with lower A1C. Consistent with previous studies, CGM devices were associated with lower A1C, as they provide patients with consistent access to glucose data that can allow the identification of glucose trends, assist in the optimization of therapies, and alarm patients to significant hyper- and hypoglycemia . Older pAlso identified in this study was a positive association between higher BMI and being self-described as African American/Black with higher A1C. The relationship between BMI and A1C may signal increased insulin resistance  in this There were several limitations to consider in this study. This was a retrospective analysis of data from patients with type 1 diabetes in a publicly available database. Medication adherence data were not available in this database. Patients in this cohort were predominately White non-Hispanic; thus, results may not be applicable to other ethnicities. Chronic disease burden, efficacy data, and reasons for prescribing adjuvant medications were not available in the database. Finally, although this was the first longitudinal study to determine the effects of adjuvant medications among patients receiving insulin for type 1 diabetes over a 7-year period in the real-world settings documented in a national database, certain small subgroups of patients may have been affected by type 2 error.Patients with type 1 diabetes who used adjuvant medications with insulin did not have significant improvement in A1C between 2010–2012, 2015–2016, and 2016–2017, when compared to an insulin-only cohort. BMI significantly increased in both cohorts. Total daily insulin use modestly decreased in the insulin-only cohort from 2015–2016 to 2016–2017. Use of adjuvant medications among patients with type 1 diabetes was not associated with improvements in A1C, BMI, or TDI in this study. Older age, higher income, greater education level, and use of a CGM was associated with lower A1C. Higher BMI and patients who self-described as African American/Black were associated with higher A1C. These data suggest that the findings from randomized controlled studies may not be consistently reproduced in the real-world settings over an extended period. This is the first study to examine the effects of various adjuvant medications among patients with type 1 diabetes in a real-world setting. Future studies evaluating the impact of adherence to medications among patients with type 1 diabetes may illustrate the effects and benefits of these medications. Given the results of the current study, healthcare providers should consider targeted lifestyle and disease management approaches and the use of insulin pumps and CGM devices to improve A1C, BMI, or TDI among patients with Type 1 diabetes.


One hundred patients aged 18–65 years planning for gastrointestinal endoscopy were divided into four groups randomly: esketamine 0, 0.15, 0.25 and 0.5 mg/kg groups (n = 25). Propofol doses followed the Dixon and Massey up-and-down method with different starting between groups. The primary endpoint was the EC50 of propofol. Secondary outcomes included the cumulative dose of propofol, the duration of the procedure, recovery time, and adverse effects. The EC50  of propofol was significantly less in the esketamine 0.5 mg/kg group compared with the esketamine 0, 0.15, and 0.25 mg/kg groups . The total dose of propofol (mean ± SD) required for the whole procedure was significantly less in the esketamine 0.5 mg/kg group compared with the esketamine 0, 0.15, and 0.25 mg/kg groups . The recovery time was significantly longer in esketamine 0 and 0.5 mg/kg group compared with other two groups (p < 0.001). More patients in the esketamine 0.5 mg/kg group experienced visual disturbance compared with the other groups (p = 0.016). Additionally, the incidence of hypotensionin the esketamine 0 mg/kg group after inducation was higher compared with other groups (p < 0.001). In summary, the administration of esketamine significantly and dose-dependently reduced the dose of propofol required to accomplish procedures.We designed a four-arm randomized controlled trial to investigate the median effective concentration (EC Gastroscopy and colonoscopy are important and common endoscopic methods for the diagnosis and treatment of gastrointestinal and colorectal diseases, such as the screening of early gastric cancer. They are invasive procedures and may cause stress reactions in the body, such as nausea, retching, vomiting, abdominal pain, abdominal distension, tachycardia and hypertension . PainlesDespite widespread use and expertise, there is no standardized drug protocol for this condition . How to Esketamine is an N-methyl-D-aspartate receptorantagonist, has similar pharmacological effects to ketamine, such asanaesthetic, analgesic and sympathomimetic properties . Studies50) of propofol incombination with different doses of esketamine and explore the type and frequency of adverse events associated with the propofol-esketamine dose combinations.Therefore, we conducted a double-blind randomised controlled trial to determine the median effective concentration . After informed written consent of patient, we included 100 subjects aged 18–65 years, American Society of Anesthesiologists (ASA) class I or II, BMI 18.5–27.9 kg/m2, undergoing painless gastrointestinal endoscopy. Exclusion criteria included: 1) allergy to any of the drugs used in the study; 2) preoperative use of analgesic and sedative drugs, or have a history of heavy smoking, alcohol abuse, serious drug abuse, and severe systemic infections; 3) renal or hepatic dysfunction; 4) a history of unregulated or malignant hypertension, significant heart disease, elevated intracranial pressure and intraocular pressure, psychiatric disease, pregnancy or suckling period.This single-centre, prospective, randomised, controlled, double-blinded trial was performed from January 2022 to August 2022 at Shanxi Bethune Hospital, Shanxi, China. The protocol was approved by the local ethics committee  and registered at Chinese Clinical Trial Registry , 0.15, 0.25, or 0.5 mg/kg. Randomisation envelopes containing the study medication data were prepared. The envelopes were opened by the single study investigator (C.P.) preparing the study medication immediately before the patient arrived in the operating theatre. The investigator collecting data, patients and their guardians, endoscopists and nurses were blinded to the study medication. The allocation sequence was not available to any member of the research team until databases had been completed and locked.All patients were fasted at least 8 h before the gastrointestinal endoscopy. After intravenous access was obtained, an infusion of 250 ml NaCl 0.9% was started. Pulse oximetry, electrocardiography and blood pressure were routinely monitored, and patients were asked to position the mselvesin lateral decubitus position, and oxygen was given for 4 L/min through a nasal catheter. The study investigator (C.P.), who did not participate in anesthesia during the endoscopy, prepared the study medications. The dose of esketamine was drawn into a 10 ml syringe and saline was added to produce a 10 ml solution. Patients were sedated using a TCI of propofol . We used the Graseby 3500  infusion pump preprogrammed for propofol TCI in the Marsh model.Lidocaine 1%, 0.05 mg/kgwas administered i.v. to minimisepain from propofol injection . Approxi50 of propofol (50 (We used an “up-and-down” titration method as described to determination of ECpropofol . For eacofol (50  (Hayes eofol (50 . A posit2) were monitored constantly until discharge, and the non-invasive arterial blood pressure (SBP/DBP/MAP) was measured at three minutesintervals throughout the procedure. All vital signs were recorded at the following six time points: before induction of anesthesia , at the end of anaesthesia induction (T1), at the time of endoscope insertion (T2), at the end of endoscopy (T3), when patients arrived at PACU (T4) and when patients awoke (T5). Patients with severe hypotension (systolic blood pressure [SBP] of <80 mmHg) or bradycardia (HR of <45 bpm) during the procedure who required rescue cardiovascular drugs were excluded from the study, the same effect-site concentration was used for the substitute patient.The heart rate (HR), and pulse oxygen saturation , Recovery time is defined as the time when propofol is stopped until the patient is awake without associated complications.2 < 90%, lasting for at least 10 s), need for airway management: such as jaw thrust, insertion of oropharyngeal or nasopharyngeal airway, or positive pressure ventilation. During the recovery room, the following adverse events were recorded: the occurrence of nausea/vomiting, dizziness, visual disturbance, such as blurred or double-vision, nystagmus, and hallucinations.During the procedure, the following adverse events were recorded: the occurrence of hypotension, the occurrence of respiratory depression . Continuous data are reported as mean (SD), or median and interquartile range, as appropriate. Categorical data are reported as frequencies (%). A value of p < 0.05 were considered statistically significant. The analysis was performed using SPSS for Windows .Numerical data were analyzed using a one-way ANOVA, Bonferroni method was used for comparison tests between groups, and categorical data were analyzed with the χ50 calculation, and anesthesia trials using Dixon’s up and down method typically have 20–40 patients  of propofol was significantly lower in the esketamine 0.5 mg/kg group compared with the esketamine 0, 0.15, and 0.25 mg/kg groups   compared with T0. At the end of anesthesia induction (T1), the HR in esketamine 0.5 mg/kg group was significantly higher than that in esketamine 0, 0.15, and 0.25 mg/kg groups (p < 0.05). There was a significant decrease in MBP in esketamine 0 and 0.15 mg/kg groups after finishing induction (T1), MBP of T1, T2 and T3 were significantly lower than that of baseline in the esketamine 0 mg/kg group (p < 0.05). While in the esketamine 0.5 mg/kg group, there was a significant increasein MBP after finishing induction (T1), and the MBP of T1 significant higher than those of T0, T2, and T3 of the same group (p < 0.05). The MAP was significantly lower in the esketamine 0 mg/kg group compared with the other three groups after finishing induction (p < 0 .05). In contrast, The MAP was significantly higher in the esketamine 0.5 mg/kg group than in the other three groups (p < 0.05).As shown in During the procedure, there was no significant difference in the occurrence of respiratory depression, nausea/vomiting, and dizziness among groups. More patients in the esketamine 0.5 mg/kg group experienced visual disturbance compared with the other groups. In addition, the incidence of hypotension in the esketamine 0 mg/kg group after inducation was higher compared with other groups . All the50 of propofol to achieve satisfactory sedation conditions decreased as the dose of esketamine increased from 0 to 0.5 mg/kg in ASA I or II patients. Patients who received esketamine 0 mg/kg or 1 mg/kg had a high incidence of either propofol or esketamine-related adverse events.We have demonstrated that the EC50 of propofol anaesthesia during gastrointestinal endoscopy in adults. Propofol is often used in combination with ketamine and opioids to improve efficacy and patient tolerance. Several studies . Finally, only patients with ASA physical status I-II were included in this study; therefore, the results are not applicable to clinically critical patients.There are some limitations to the study that should be mentioned. The first is the lack of esketamine dose more than 0.5 mg, such as 1 mg/kg, the choice of esketamine doses was based on the clinical experience of the investigators and previous studies. Although a higher esketamine dose can reduce propofol dose and associated side effects more significantly, it may also significantly increase the incidence of esketamine-related adverse effects. The cognitive function changes and psychomimetic side effects caused by esketamine have been widely concerned, which could be dose-related . Second,In conclusion, this report suggests that increasing the dose of esketamine can significantly reduce the dose of propofol required to accomplish procedures, with a corresponding reduction in propofol associated hemodynamic changes.


Typically, prognostic capability of gene expression profiling (GEP) is studied in the context of clinical trials, for which 50%‐80% of patients are not eligible, possibly limiting the generalizability of findings to routine practice. Here, we evaluate GEP analysis outside clinical trials, aiming to improve clinical risk assessment of multiple myeloma (MM) patients.A total of 155 bone marrow samples from MM patients were collected from which RNA was analyzed by microarray. Sixteen previously developed GEP‐based markers were evaluated, combined with survival data, and studied using Cox proportional hazard regression.P < .001) and 5.8 [2.7‐12.7] (P < .01) for overall survival (OS). A multivariate model proved only SKY92 and the PR‐cluster to be independent prognostic factors compared to cytogenetic high‐risk patients, the International Staging System (ISS), and revised ISS. A substantial number of high‐risk individuals could be further identified when SKY92 was added to the cytogenetic, ISS, or R‐ISS. In the cytogenetic standard‐risk group, ISS I/II, and R‐ISS I/II, 13%, 23%, and 23% of patients with adverse survivals were identified.Gene expression profiling‐based markers SKY92 and the PR‐cluster were shown to be independent prognostic factors for survival, with hazard ratios and 95% confidence interval of 3.6 [2.0‐6.8] (For the first time, this study confirmed the prognostic value of GEP markers outside clinical trials. Conventional prognostic models to define high‐risk MM are improved by the incorporation of GEP markers. In current practice, cytogenetic aberrations measured by fluorescence in situ hybridization (FISH) and the international staging system (ISS) are most common for MM prognostication.Markers based on gene expression profiling (GEP) have shown promising results to predict clinical outcome. For example, SKY92—a biomarker that classifies MM patients into high‐ or standard‐risk based on the expression of 92 genes—has shown to correlate significantly with survival in multiple trial data sets.In this retrospective, multinational study outside of clinical MM trials, we show that GEP‐based markers provide an accurate prognostic distinction between risk groups that better reflects survival, as compared to ISS, R‐ISS, and cytogenetics. Moreover, by combining the SKY92 classifier with conventional markers, clinical risk assessment of the MM patient can be improved.22.1Patients in this study were part of the Horizon 2020 funded MMpredict project, of which this current analysis included the subset of 155 patients treated outside clinical trials: 73 patients from the National University Health System, Singapore (NUHS), 37 from the Munich Leukemia Laboratory, Germany (MLL), and 45 from the University of Turin, Italy (UNITO). Patients aged 18 years or older and diagnosed with MM were included. Informed consent and ethical approval by institutional review boards were obtained, in accordance with the Declaration of Helsinki. Clinical information was obtained from patients' health records, including chromosomal abnormalities detected by interphase FISH for t, t, t/t, del(17p), del(13q), and gain(1q) as reported by the local laboratories, and generally reflecting the FISH guidelines.2.2Plasma cells were purified from bone marrow aspirates using CD138+‐based immunomagnetic bead selection , stored in RLT buffer, and are only used if ≥80% CD38+ cells. RNA was isolated using a DNA/RNA AllPrep kit  according to the manufacturer's instructions. RNA concentration was measured using the NanoDrop spectrophotometer (Thermo Fisher Scientific), and quality and purity were assessed by the RNA 6000 assay . A minimum of 100 ng total RNA was used as test input.2.3Gene expression profiling data were generated at a central laboratory (SkylineDx). RNA processing, target labeling, and hybridization to gene expression arrays were performed on the Human Genome U133 Plus 2.0 platform (Thermo Fisher Scientific) following the MMprofiler™ protocol (SkylineDx).2.4P‐values below .05 were considered to be significant through this study.Univariate and multivariate survival analyses were performed by the Cox proportional hazard model provided that the proportional hazard assumption was met based on weighted residuals . Hazard ratio estimates were expressed relative to the lowest‐risk group and assessed by a two‐sided Wald test. Analysis of deviance was performed with the “stats” package (v3.6.3). 33.1The 155 MM patients, treated outside of clinical trial setting, had a median age of 66 years  did not correlate with either one of the three sites of inclusion Figure . Compareos) and PFS (HRpfs), and corresponding 95% confidence interval (CI) of HRos: 3.6 [2.0‐6.8] (P < .001) and HRpfs: 2.4 [1.5‐4.0]  and HRpfs: 3.0 [1.4‐6.4] (P < .01), although its sample size of 9 patients was small (Table os: 2.0 [1.0‐3.9] (P = .04), but not for HRpfs: 1.4 [0.87‐2.4]  and HRpfs: 1.0 [0.5‐2.1]  and HRpfs: 2.0 [0.66‐6.1] , HRpfs: 2.3 [1.1‐4.6] for SKY92, and a HRos: 3.7 [1.5‐9.1] (P = .004), HRpfs: 2.5 [1.1‐5.8] (P = .04) for the PR‐cluster.In the subsequent multivariate analysis—using all significant univariate markers as input , highlighting the added prognostic value of GEP markers. SKY92 overarched the classification of the PR‐cluster; 8 of 9 PR‐cluster patients were in the SKY92 high‐risk group, disabling the assessment of association with patients identified as PR‐positive and SKY92 standard‐risk  and the three high‐risk FISH markers, patients were identified as single (n = 23), double (n = 19), and triple (n = 4) hit disease . On the other hand, the addition of CA, ISS, or R‐ISS to a model with SKY92 alone was only significant for ISS . For PFS, adding SKY92 to any of the existing markers always improved the model , while adding existing markers to SKY92 did not result in significant improvements .In an analysis of variance, it was shown that adding SKY92 to the OS Cox models significantly improved all stratifications (os: 12.3 [4.8‐31.9] (P < .001) compared to the SKY92 standard‐risk group . Within CA high‐risk patients, the 92‐gene classifier was not significant with a HRos: 0.94 [0.35‐2.5] . By incorporating GEP‐based classifiers, additional high‐risk patients were detected that were not identified by other markers. These high‐risk patients had associated adverse outcomes and could potentially be referred to clinical trials focusing on this subpopulationMultiple studies have made it apparent that data from randomized controlled studies may not always reflect results for patients undergoing treatment in routine clinical practice.P‐values, potentially reflecting the new source of information (tumor gene expression) not captured by the other markers focusing on tumor genetics and more systemic host factors. These results support previous findings in patients in clinical trials.From the list of evaluated biomarkers, the association of SKY92 high‐risk and PR‐positive patients with poor OS and PFS had the strongest prognostic effect, demonstrating their accuracy and reliability in this cohort reflecting a "real‐world" scenario. Compared to frequently applied clinical risk indicators, both GEP‐based markers had more pronounced hazard ratios with smaller In contrast to conventional markers, gene classifiers better capture the global state of a tumor. For example, in a IGH translocation, a gene is juxtaposed the IGH‐locus and expected to become overexpressed. There might, however, be mechanisms counteracting the overexpression, such that the actual expression of the translocated (or co‐regulated) genes might be more informative. In addition, a single gene classifier is likely to target multiple prognostic mechanisms. It has been shown that the 92 genes in the SKY92 are enriched for the long arm of chromosome 1.In contrast to SKY92, which held up in this cohort as compared to its validation in trial patients published previously,Because it is unavoidable that some patients with adverse survival will be classified into the SKY92 standard‐risk group and/or patients with favorable survival in the SKY92 high‐risk group, it proved useful to combine multiple prognostic markers to strengthen their prognostic power. Analysis of variance has shown that adding SKY92 to any of CA, ISS, or R‐ISS significantly improved the stratification for OS, while only addition of ISS to a model with SKY92 was significant, confirming one of our previous findings.To conclude, this multinational study in MM patients treated outside clinical trials recapitulates GEP‐based information as strongly prognostic for both OS and PFS. In addition, we found that the prognostic indications of CA, ISS, and R‐ISS are improved by the addition of SKY92, indicating the added clinical value of gene‐based technology to personalized management in MM patients.YTC, ETV, EHB, RK, and MHV are employees and option holders of SkylineDx BV. SO: Honoraria: Janssen, Takeda, Amgen. Advisory Board: Adaptive Biotechnologies, Amgen, Takeda, Janssen. TH is part owner of MLL Munich Leukemia Laboratory. WJC has no relevant conflict of interest to this work. PS: Takeda: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Skyline: Research Funding; Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding. AL: Amgen: Honoraria, Bristol‐Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Janssen: Membership on an entity's Board of Directors or advisory committees, Honoraria, Takeda: Membership on an entity's Board of Directors or advisory committees.The authors confirm contribution to the paper as follows: EHvB, MHvV, PS, and AL conceived the project and design. SO, TH, W‐JC, PS, AL, and the SkylineDx lab contributed to the collection of the data. Y‐TC, ETV, EHvB, RK, and MHvV performed the analysis and interpretation of initial results. Y‐TC and ETV drafted the manuscript. All authors reviewed and commented on the results and approved the final version of the manuscript.Supplementary MaterialClick here for additional data file.


The MO patients underwent sleeve gastrectomy (SG) in 62 (40.3%) cases, laparoscopic Roux-and-Y gastric bypass (LRYGBP) in 85 (55.2%) cases and One-Anastomosis Gastric Bypass (OAGB) in 7 (4.5%) cases. underwent OAGB. The patients in the SO group were submitted to SG in 11 (57.9%) cases, LRYGBP in 5 (26.3%) cases, and OAGB in 3 (15.8%). At 24-month follow-up, an excess weight loss (EWL) >50% was achieved in 129 (83.8%) patients in the MO group and in 15 (78.9%) in the SO group (p = 0.53). A BMI < 35 kg/m2 was achieved in 137 (89%) patients in the MO group and from 8 (42.2%) patients in the SO group (p < 0.001). The total weight loss was significantly directly related to the initial BMI. Superobesity was identified as independent risk factor for surgical failure when considering the outcome of BMI < 35 kg/m2.We included in this study 154 MO patients, with a median preoperative BMI of 40.8 kg/mOur study confirms that, although SO patients tend to gain a greater weight loss than MO patients, they less frequently achieve the desired BMI target. In this setting, it should be necessary to re-consider malabsorptive procedures as first choice. UsuallyIn this study, we aimed to evaluate the outcomes of bariatric surgery in SO patients compared to MO patients, and to assess patients' and surgical factors associated with bariatric success.This is a unicentric, retrospective study. Data were extracted from a prospectively maintained database of all morbidly obese patients undergoing bariatric surgery in our institution.We included in our study all the patients who underwent bariatric surgery from January 2018 to July 2020 at our institution. The indications for bariatric surgery were assigned according to IFSO criteria , 14, andAll patients who underwent revisional surgery and patients without at least 12 and 24 months of postoperative follow-up were excluded.Preoperative clinical evaluation was performed to detect patient characteristics , biometric values , and comorbidities, including diabetes, metabolic, cardiac or cerebrovascular diseases, gastroesophageal reflux disease (GERD), arthrosis and Obstructive Sleep Apnea Syndrome (OSAS). Serological blood tests according to IFSO guidelines , lower ULRYGBP was usually recommended in patients who suffered from severe GERD or type 2 diabetes (T2DM), or in patients with severe sweet eating behavior. SG was the favourite procedure in case of patients younger than 30 years old, large incisional hernia or severe small bowel adhesions. OAGB was not performed routinely and was indicated only in patients with severe dyslipidemia or diabetes.LRYGBP was performed with the creation of a gastric pouch of approximately 30 cc, with a biliopancreatic limb of 75 cm, and an alimentary limb of 100–150 cm. SG was performed with gastric section 5 cm from the pylorus and 40 Fr bougie. OAGB was performed and 40 Fr bougie gastric pouch (>10 cm) and 150–180 cm biliopancreatic limb.All patients were evaluated 1,3, 6, 12, 18, 24 months after surgery. Postoperative assessment included clinical, nutritional, or psychological evaluation, and serological blood test.2, and b) EWL > 50%.The primary endpoint was surgical success at 24 months, defined as a) BMI < 35 kg/m2).As secondary endpoints were evaluated as predictive and protective factors for surgical success (EWL < 50% and BMI < 35 kg/m2 and ≤50 kg/m2). The Chi-squared test or the Fisher's exact test was used to compare categorical variables, while Student's t-test or Mann–Whitney's U test was employed for continuous variables based on the data distribution. Data are presented as medians with 95% confidence intervals or means with standard deviations (SD) as appropriate.Patients were divided into two groups according to preoperative BMI  patients had a complete postoperative follow-up at 12 and 24 months after surgery and were then included in the study.2. The SO group included 19 patients with a mean age of 41.6 ± 10.5 years and median preoperative BMI of 54.9 kg/m2.There were 30 (19.5%) male patients in the MO group and 4 (21.1%) in the SO group. Regarding comorbidities, OSAS was present in 52 (33.8%) patients in the MO group and in 11 (57.9%) the SO group, high blood pressure in 82 (53.2%) cases in the MO group and in 8 (42.1%) in the SO group, osteoarthrosis in 93 (60.4%) patients in the MO group and in 13 (68.4%) in the SO group, GERD in 70 (45.5%) in the MO group and in 4 (21.1%) in the SO group, diabetes mellitus in 23 (14.9%) in the MO group and in 3 (15.8) in the SO group. In univariate analysis, preoperative weight and BMI were significantly higher in the SO groups (p < 0.001), and the incidence of OSAS and osteoarthrosis was higher in SO group . Full preoperative data, including comorbidities, are reported in The MO group included 154 patients with a mean age of 45.4 ± 11.2 years and median preoperative BMI of 40.8 kg/mAs reported in 2 in the MO group and 36.4 kg/m2 in the SO group (p < 0.001). The TWL was 31.1 ± 11.7 Kg in the MO group, and 51.9 ± 18.3 Kg in the SO group (p < 0.001). The correlation test demonstrated that SO was significantly associated with a higher TWL ; the scatter diagram is reported in The outcomes after 24 months of follow-up are reported in p = 0.53). A BMI < 35 kg/m2 was achieved in 137 (89%) patients in the MO group and from 8 (42.2%) patients in the SO group (p < 0.001); a BMI < 30 kg/m2 was reached from 90 (58.4%) patients in the MO group and in 1 (5.3%) case in the SO group (p < 0.001).When considering an EWL > 50%, surgical success was achieved in 129 (83.8%) patients in the MO group and in 15 (78.9%) in the SO group (p = 0.0353); indeed, when considering surgical success as BMI < 35, superobesity was an independent predictive factor for failure .At multivariate analysis , when coFull comorbidity-related outcomes are reported in 2 and <30 Kg/m2 were reached significantly more frequently by the MO group. The median BMI at 24 months was 30.3 (27.1–32.2) Kg/m2 in the MO group, and 38.4 (36.5–41.1) Kg/m2 in the SO group (p < 0.0001).Finally, we performed a comparison of the outcomes at 24 months of follow-up of patients that underwent SG . The TWL2.In one of the most famous paradoxes of Zeno of Elea, Achilles, famous for his speed, will never reach the tortoise after granting her a head start. This narration appears to be similar to what happens to SO patients: in fact, SO patients start with a handicap (a greater BMI), run faster than MO patients (gaining a greater weight loss), but lose the race against them, rarely reaching the finish line of the BMI target <30 kg/m2: in fact, only 8 (42.1%) out of 19 SO patients reached this result, compared to 137 out of 154 (89%) MO patients. When considering the target of BMI < 30 kg/m2, only one SO patient (5.3%) reached the result, compared to 90 (58.4%) in the MO group. Furthermore, in our study, superobesity was identified as an independent risk factor for failure when considering the outcome of BMI < 35 kg/m2 at 24 months. However, we should note that, considering the outcome of EWL > 50% at 24 months, the results were not significantly different between the two groups. This result raises the issue whether EWL > 50% is a proper target to evaluate bariatric surgery failure, or if it would be better to consider the final BMI.In fact, our study confirmed that SO patients may present suboptimal results in terms of weight loss after bariatric surgery compared to MO patients. Our comparative analysis showed that SO patients significantly gained more weight loss during the first two years after surgery, but this was not enough to achieve surgical success in terms of BMI < 35 kg/mAccording to our results, several studies in which patients were stratified by obesity category showed that a higher baseline BMI was associated with a lower %EWL , 16, 17.p < 0.001) and mortality .Our study did not show an increased risk of postoperative complications in SO patients. In this regard, the literature is still contrasting and inconclusive. Verhoeff reported five years of MBSAQIP data for SO patients . In his p = 0.041; and 0.5% SG, p = 0.023)  than other procedures  . Only on= 0.023) .p < 0.001), and LRYGBP was used in 30% of SO patients and in 25.3% of non-SO patients (p < 0.001)  than in SG (58.7% and 59.9%) and adjustable gastric banding (38.7% and 48.3%). At 12 and 24 months, the median BMI was lower in LRYGBP (34.5 and 33.2 kg/m2) than in SG and adjustable gastric banding (p < 0.001). Tien-Chou Soong reported data on 498 SO patients, who underwent 62 RYGB, 190 SG, and 246 OAGB procedures  at 12 months . At 12 aThe choice of surgical procedure in SO patients should always be carefully evaluated and should be addressed based on surgical perioperative risk and surgical results . Considering weight-regain as the most common bariatric surgery failure in SO patients, reoperation could achieve better improvement in primary SG than primary RYGB, despite new procedures were promising .Furthermore, it would be necessary to reconsider malabsorptive procedures as long limb LRYGBP, distal OAGB, single anastomosis gastro-ileal bypass (SAGI), single anastomosis duodeno-ileal bypass (SADI-S).Furthermore, it would be necessary to reconsider mixed  procedures as long limb LRYGBP, distal OAGB, single anastomosis gastro-ileal bypass (SAGI), single anastomosis duodeno-ileal bypass (SADI-S).Our study has some limitations. First, this was a retrospective study. Then, the number of patients in the SO group was limited, and it is likely that the number was not sufficient to reach the statistical power to detect significant differences between the two groups regarding some aspects, including postoperative complications and comorbidity resolution. Furthermore, in our multivariate analysis SG was an independent risk factor for surgical failure, but the analysis was conducted considering both the SO and MO patients. For these reasons, the results of our study should be prudently considered.2 tend to lose more weight than MO patients, but less frequently reach the goal of BMI < 35 kg/m2. Even if our study has several limitations, mainly the limited number of SO patients, SG seems to be associated with an increased risk of surgical failure, particularly in SO patients. In this setting, it could be necessary to re-consider mixed procedures; however, the type of intervention should be chosen based on the patient's characteristics, according to the rule that “one size does not fit all”.The management of SO patients is an important issue. Our study confirmed that, after surgery, patients with BMI > 50 kg/m