Despite currently available, scientifically proven treatments and national guideline, the SAM recovery rate is still considerably behind expectations, and it continues to have a devastating impact on under-five children. Identifying predictors of time to recovery might help to reach the minimal criterion established by the WHO and the national Sphere which decreases child mortality. Therefore, the current study assessed time to recovery and its predictors among children aged 6–59 months admitted with SAM in the Healthcare Setting of Southwest Ethiopia, 2021.An institutional-based multicenter retrospective follow-up study was conducted on 486 children aged 6 to 59 months admitted with SAM cases. Data were entered into Epi-Data version 4.6 and exported to Stata version 14 for further analysis. Cox–Snell residual plot was used to assess the final model’s overall goodness of fit. Finally, a significant predictor of time to recovery was identified using Weibull survival regression model, at 0.05 significance level.Overall, 68.72  of the children recovered and 4.32% died. The overall incidence density was 3.35/100-person day. Independent predictors of time to recovery were, starting complementary feeding at six months , pneumonia at baseline , amoxicillin , and folic acid supplementation .The recovery from SAM at study area after a maximum of 60 days of treatment was below the accepted minimum standard. Complementary feeding, pneumonia, treated by amoxicillin, and folic acid supplementation were predictors of time to recovery. Therefore, providing folic acid and amoxicillin for those in need as well as the earliest possible treatment of concomitant conditions like pneumonia is highly recommended.The online version contains supplementary material available at 10.1186/s41043-022-00331-9. Malnutrition refers to energy and/or nutrient deficits, excesses, or imbalances in a person’s diet . DependiDespite the fact that stunting has been on the decline since 2000, wasting continues at alarming rates . An estiIn Ethiopia, over 25,000 children with SAM are admitted to hospitals each month  and it aStandardized guidelines for the care of SAM patients have been developed by the WHO  and EthiDifferent previous studies identified anemia, tuberculosis (TB), deworming supplementation, malaria, pneumonia, history of bottle feeding, and vaccination status as the most common predictors of time to recovery (increase recovery time) from SAM , 27, 28.Thus, this study was aimed to determine the time to recovery from SAM and its predictors among children 6–59 months of age admitted at hospitals of Buno Bedele and Ilu Aba Bor zones, southern Ethiopia.An institutional-based multicenter retrospective follow-up study was conducted on SAM cases enrolled at the hospitals from September 09, 2017, to April 04, 2021, for 3 years and half. The study was conducted at 3 hospitals: Darimu Primary Hospital (DPH), Bedele General Hospital (BGH), and Mettu Karl Comprehensive Specialized Hospital (MKCSH), Oromia Regional State, South West Ethiopia. DPH currently serves a total of 434,158 people from two districts, with 2269 pregnant women and 10,040 under-five children scheduled for services each year, while MKCSH serves a total of 1.2 million people, with 6270 pregnant women and 27,750 under-five children scheduled for services. ways, BGH serves a total of 841,158 patients annually, including 4395 pregnant women and 19,452 children under the age of five.All children aged 6–59 months who were treated for SAM at the inpatient therapeutic feeding center of DPH, BGH, and MKCSH was the source population, whereas all children aged 6–59 months admitted with SAM at the inpatient TFC of the hospitals from September 09, 2017, to April 04, 2021, were our study population. All SAM cases with congenital problems, transferred out cases, other causes of edema, and readmitted cases within the study period were excluded.The sample size was calculated using the Schoenfeld formula using Stata software version 14 considering predictors significantly associated with the time to recovery from SAM from prior studies.where p1 is the proportion of subjects in the exposure group, E is the number of required events, n is the sample size, and HR is the hazard ratio of selected covariates. We computed the sample size by taking the hazard ratio for covariates significantly associated with time to recovery from SAM from studies in the northwest  and soutEligible cases were selected using the existing medical registration numbers using a systematic random sampling technique to generate the required sample size, starting from the most recent month and going backward, based on the sequence of medical card numbers. The total sample size for each hospital was allocated proportionally, and the schematic is presented in Fig. 1Time to recovery from SAMSocio-demographic and admission characteristics Referral address (place of treatment), stabilization center, fully vaccination to age, mixed feeding, NG tube feeding, breast feeding status, bottle feeding, received play stimulation, child age, sex, admission and discharge date residence, admission season.Therapeutic foods administered Plumpy nut, F75 intake, F100 intake, not entering phase 2 on day 10 and being admitted to referral hospitals.Routine medication and treatment Vitamin A supplement, use of oral antibiotics (amoxicillin), deworming, special medications .Anthropometric: Daily weight gain, MUAC, not lose edema within four days of inpatient treatment.Medical Comorbidities TB, Human immune deficiency virus (HIV), pneumonia, anemia, malaria, diarrhea, septic shock, altered body temperature, nutritional status .Recovered children whose medical records were classified as cured or recovered after overcoming medical complications and edema and achieving and maintaining MUAC (12.5 cm) and WFH (85 percent) of the median WHO growth chart reference  per 100 PD after total 9970 PD observation. At the institution, the recovery rates at DPH, BGH, and MKCSH were 2.68 , 5.12 , and 3.55  per 100 PD, respectively. The cumulative survival probability of patients was 0.875 at 14 days, 0.491 at 21 days, 0.297 at 28 days, and 0.0212 at the end of the follow-up period , which indicates the satisfaction of PHA . Children who were admitted without pneumonia were 1.33 times more likely to recover as compared to those who had pneumonia at baseline as a comorbidity . Children who took amoxicillin had a 1.31  times greater likelihood of fast recovery than their counterparts. Children with SAM who received folic acid were 1.82 times more likely to recover early  than children who did not receive folic acid  with incidence density of 3.35 per 100 PD with 95% CI of  after total 9970 PD observation. This finding is below the minimum accepted international standard of 75% . This fiThe results of similar investigations carried out in Pawi General Hospital  and AksuThe current study revealed that children who had appropriate complementary feeding at age were more likely to recover from SAM compared with children who did not have it. This finding is supported by WHO guideline  and otheThis study also demonstrated that children with SAM who did not get pneumonia had a nutritional recovery rate that was noticeably higher than that of kids who did. Consistent with this finding, a hospital-based retrospective follow-up studies in Jimma , Addis AAccording to this study’s findings on antibiotic therapy, children who received amoxicillin had a better likelihood of recovering from SAM than their counterparts. It was in line with a study carried out at the Jimma University Medical Center in southwest Ethiopia . SimilarMain strengths of the paper include the use of follow-up study design and advanced statistical analysis . This study had some limitations, such as the fact that all of its data came from secondary sources and there was no way to control the accuracy of the measurements made while the patients were in the hospital. Additionally, it was unable to look at the socioeconomic status and influences of the parents and guardians. Furthermore, this study did not take into account patient management-related variables including medical equipment and professional expertise that could have an impact on the outcome variable.The overall recovery rate from SAM fell short of the internationally recognized minimal standard (> 75%). Factors like complementary feeding status of the child, presence of pneumonia, treated by amoxicillin, and folic acid supplementation, significantly predict the recovery time of SAM. Therefore, it is recommended to concentrate on enhancing early detection and referral of SAM children to treatment facilities. While doing so, the provision of folic acid and amoxicillin, as well as the earliest possible treatment of concomitant conditions like pneumonia, should be emphasized by healthcare professionals in order to shorten SAM’s recovery time.Additional file 1. Model diagnostics for predictors of time to recovery from SAM among age 6-59 months children admitted at TFC of Southwest Ethiopia.


Childhood adenoid hypertrophy (AH) is common and is often associated with allergic asthma, resulting in complications like obstructive sleep apnea syndrome (OSAS). Management of the disease and its complications is often challenging.We report here a case of a 10-year-old boy who suffered from severe allergic asthma and rhinitis and was treated with omalizumab. Before the treatment, the childhood asthma control test , visal analog scale  and lung function  were seriously affected. Polysomnography showed OSAS , low hypooxia saturation , and adenoid hypertrophy (at grade III). After treating with omalizumab for 4 weeks (once treatment), the ventilation function, symptoms of asthma and allergic rhinitis , and OSAS (AHI: 1.8 and LoSpO2: 92.6%) were all improved, and the adenoids size was also significantly reduced to grade II. And during the following 3 times of treatment, the allergic symptoms continued improving, and the size of adenoid was reduced to grade I. Even 6.5 months after cessation of omalizumab, the size of adenoid remained at grade I.This is the first documented case that childhood adenoid hypertrophy can be significantly improved by omalizumab. Adenoidal hypertrophy (AH) is a common phenotype in children that is associated with environmental allergies and/or infections. AH often causes obstructions of nasal passages and eustachian tubes, leading to dysfunctional nasal mucus clearance and may result in craniofacial anomalies. AH is also associated with obstructive sleep apnea syndrome (OSAS),  which ma1% pred), 90.5% for forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), 54.2% and 37.4% for the forced expiratory flows at 50% and 75% of forced vital capacity (FEF50 and FEF75), and 49.1% for the maximal mid-expiratory flow (MMEF75/25). The total immunoglobulin E (IgE) was 474 kU/L.A 10-year-old boy  with AH, severe allergic asthma and perennial AR was admitted to our clinic. At the first visit, the patient demonstrated uncontrolled airway hypersensitivity, with symptoms of frequent nasal obstruction, rhinorrhea, cough, and wheezing. The score of childhood asthma control test (C-ACT) was 14 (not well-controlled) and the score of global evaluation of treatment effectiveness (GETE) was 4 (poorly effective). Pulmonary function testing showed that the patient had mild obstructive ventilation dysfunction and moderate to severe dysfunction in ventilation in small airways, with 77.2% for the forced expiratory volume in the first second in percent predicted values  guidelines [According to the idelines , the patAfter the treatment for more than three months, the symptoms remained uncontrolled, with the unsatisfied pediatric asthma quality of life questionnaire (PAQLQ) score of 121 . Becaus2) was 70%. These results strongly suggested that the child had OSAS.The patient was assessed for upper airway inflammation before the treatment with omalizumab. The visual analog scale (VAS) score of allergic symptoms was 7 and the pediatric sleep questionnaire (PSQ) score was 6. Flexible fibro-optic endoscopic examination of the nose and nasopharynx was performed, with AH rated as grade III Fig. A, accord1% pred 86.3%, FEV1/FVC 97%, FEF50 74.6%, FEF75 51.1%, and MMEF75/25 67.1%. Additionally, the size of adenoid tissue went down to grade II  of omalizumab treatment, the symptoms of asthma and AR were relieved significantly, with C-ACT score of 24, PAQLQ 141, VAS 2 and GETE 1 (complete control of asthma). In addition, pulmonary function was also improved dramatically, with FEVThe size of adenoids increases with age, and normally reaches its maximum by age 6 to 7 years, followed by its regression before adolescence. However, abnormal enlargement or delayed degeneration of adenoid tissues in children is associated with various complications, such as lethargy, poor academic performances, nocturnal enuresis, attention deficit hyperactivity disorder, hypoxemia and so on. At present, adenoidectomy is the definitive method for managing AH worldwide, but it may be associated with a variety of complications, including hemorrhage, infections, palate dysfunction and the risks associated with general anesthesia. In addition, there is still a possibility of recurrence after surgery . TherefoOmalizumab was the first approved anti-IgE drug for the treatment of asthma in the world, with very successful applications in treating allergic diseases in childhood as well as adults. Evidence suggests that omalizumab is not only very effective in reducing the symptoms of asthma itself, but also effective in improving the outcomes of other coexisting conditions, such as AR, allergic conjunctivitis, and atopic dermatitis . In thisThere are no relevant reports on the treatment of childhood AH with omalizumab to date. This is the first attempt to test the drug for disease in a child. We have found that omalizumab not only improved symptoms, but also reduced the size of adenoids. These observations, if confirmed, could undoubtedly present pediatricians with a new tool in managing children AH and allergic diseases.


China, the largest global CO2 emitter, proposes to peak carbon emissions by 2030 and become carbon neutral by 2060; transforming the energy structure represents one of the primary means of addressing carbon emissions; thus, it is essential to investigate the impacts of alternate energy sources throughout the country. Based on energy consumption and carbon emissions data from 30 provincial-level administrative regions in China , the study here investigated the shares of coal, petroleum, natural gas, and non-fossil energy sources , as they relate to total, per capita, and per unit GDP CO2 emissions via spatial regression. The results showed that: (1) The epicenters of coal and carbon emissions have shifted from the east to the central and western regions; (2) There is a significant correlation between energy structure and carbon emissions: coal has a positive effect, petroleum’s effects are positive at first, and negative subsequently; while both natural gas and non-fossil energy sources have a negative impact; (3) Provincial-level carbon emissions are affected by energy structure, carbon emissions in neighboring regions, and other factors.To mitigate climate change, reducing carbon dioxide (CO Indeed, China’s CO2 emissions per unit gross domestic product (GDP) exhibited a continuous downward trend between 1980 and 2019, falling from 5.23 to 1.08 tC· CNY 10,000−1 (Chinese Yuan—CNY); although, total CO2 emissions and per capita rates increased over the same period, rising from 1.38 to 9.58 billion tC, and from 1.39 to 6.79 tC·persosn−1, respectively  accounting for 28.93% of global emissions, 94.60% of which was in the form of COectively . Despiteectively .2 emission indicators. Hence, there remains a need to assess the impacts of energy structure on total, per capita, and per unit GDP CO2 emissions, thereby informing future carbon reduction policies in China for combatting global climate change.Moreover, these proportional shifts were accompanied by variable effects on the COWhile studies regarding the impacts of individual energy components on carbon emissions remain limited, there has been an abundance of research on factors influencing carbon emissions, the majority of which indicating that energy structure is one of the most important factors influencing carbon emissions. The main research methods used in such studies were factor decomposition and econometric analyses, where the former tends to use the logarithmic mean Divisia index (LMDI) for studying the impacts of dynamic changes in various factors, such as energy consumption intensity and structure, as well as industry structure, GDP, and population, on carbon emissions ,10,11,122 emissions and the level of economic development displayed a significantly inverted U-shape [2 emissions throughout China, respectively [In China, several studies have focused on the effects of population growth, industries, urbanization, energy structure, and energy consumption intensity on carbon emissions. Du constructed a panel data model of provincial-level carbon emissions, finding that the shares of coal, heavy industry, as well as urbanization levels, have a significant positive correlation with carbon emissions in China, whereas the relationship between per capita CO U-shape . Both Ya U-shape ,24. Alteectively ,26. Otheectively ,32,33,342 emissions at the provincial level in China, and (2) To investigate the impact of energy structure on carbon emissions.Accordingly, it has been revealed that population growth, industries, and urbanization affect carbon emissions through energy structure and consumption. Since total energy consumption is primarily influenced by various aspects, including both economic and technological factors, the sector cannot directly dictate total energy consumption, but only manage the supply structure. Accordingly, as carbon reduction mechanisms are explored from the perspective of the energy sector, the research here has analyzed panel data on provincial-level energy structure and carbon emissions in China using a geographically weighted regression model. The purpose of the present study was twofold: (1) To analyze the characteristics of energy structure and CO2 emissions coefficients were acquired from the 2019 IPCC Guidelines for National Greenhouse Gas Inventories. These datasets were processed using ArcGIS v.10.6 and GeoDa v.1.16.Since only the data of China’s provincial carbon emissions from 1997 to 2019 can be collected at present, all data used in this study are from that period, a total of 23 years. Here, population and GDP data for the 23-year period since 1997 were sourced from the China Statistical Yearbook (1997–2019), while total energy consumption and energy structure data were obtained from the China Energy Statistical Yearbook (1997–2019). Carbon emissions data were obtained from the China Emission Accounts and Datasets (1997–2019), whereas CO3 kJ or 7000 kcal . The first step in data processing was standardizing the measurement units of energy sources. Here, various types of energy sources were converted to the heat unit measurement of tons of coal equivalent (tce), where one tce is equal to 29,307.6 ∗ 102 emissions coefficients of non-fossil energy sources were zero, their correlated impacts on emissions were considered negligible. Furthermore, the consumption of non-fossil fuel energy sources was typically presented as a single total in statistical yearbooks, and could not be further partitioned out into individual sources; thus, the analyses of non-fossil energy sources here were combined into a single category.Second, the total energy consumption from coal, petroleum, natural gas, and non-fossil fuel energy sources was calculated. Total coal consumption was taken as the sum of consumption for various coal product types, including: (1) raw coal, (2) clean coal, (3) other washed coal, (4) briquettes, (5) coke, (6) coke oven gas, (7) other gasses, (8) other coking products, and (18) heat; whereas total petroleum consumption assessed: (9) crude oil, (10) gasoline, (11) kerosene, (12) diesel oil, (13) fuel oil, (14) liquefied petroleum gas—LPG, (15) refinery gas, and (16) other petroleum products. Natural gas consumption was obtained directly from the relevant data on (17) natural gas. All non-fossil fuel energy sources—hydro-, nuclear, wind, and solar power—consisted of two parts: (18) electricity and (19) other energy. Based on provincial-level statistical yearbooks published throughout China, hydro- and nuclear power consumption were included under (18) electricity, while wind, solar, and biomass consumption were placed within (19) other energy. Notably, electricity consumption, represented here solely by hydro- and nuclear power consumption, excluded fossil-fuel power consumption. Since COThe formula for energy structure was expressed via Equation (1):2 emissions coefficients used for calculation [The sourced carbon emissions data from the China Emission Accounts and Datasets were calculated based on energy consumption, and thus served as the COculation ,36,37,38I indicator [I was used for detecting spatial correlations across China, to identify overall spatial clustering and characteristics of carbon emissions . The formula for global Moran’s I is shown in Equation (2):2 emissions; 2 emissions of the 30 provincial-level administrative regions; I range between −1 and 1, where a positive (negative) value represents a positive (negative) correlation, and the greater the absolute value, the stronger the spatial correlation. A global Moran’s I of zero indicates no spatial correlation.The study here assessed the spatial correlations and variations between units of analysis from both global and local perspectives, commonly represented by Moran’s ndicator . Global I is expressed via Equation (3):2 emissions in provinces As local spatial autocorrelation can be used to identify clusters of similar values among different provinces, it addresses the shortcoming that global spatial autocorrelation cannot reflect spatial clustering characteristics within regions. Accordingly, the formula for local Moran’s v.1.16.Ordinary least squares models ) have been adopted by previous studies since the spatial effects of dependent variables are generally considered independent and identically distributed; however, this can lead to the incomplete interpretation of regression results, as it neglects potential spatial effects . Here, tOLS, SLM, SEM, and SEMLD were expressed via Equations (4)–(7), respectively:Since only data on carbon emissions from 1997 to 2019 can be collected, to demonstrate the spatiotemporal characteristics of China’s energy structure more clearly from 1997 to 2019, a visual analysis of the energy structure data was conducted, and the results were illustrated across five-time sections: 2000, 2005, 2010, 2015, and 2019. The results of these analyses are detailed as follows:2 emissions in China between 1997 and 2019, this study conducted further visual analyses of the data, and illustrations are provided across five years: 2000, 2005, 2010, 2015, and 2019. The results of these analyses are detailed as follows:To more clearly demonstrate the spatiotemporal characteristics of total, per capita, and per unit GDP CO2 emissions in China have been increasing consistently since 2000, as the number of provinces with >2 million tCO2 emissions has risen from only four in 2000, to 27 in 2019, thereby encompassing most of the provinces in China. The center of peak total CO2 emissions has gradually shifted from the eastern region (Bohai Rim) to the central and western regions, while the province with the highest total CO2 emissions has changed from Liaoning to Shanxi over the past two decades. Regarding the pace of change, total CO2 emissions rose at their fastest between 2000 and 2010, but slowed significantly around 2019  to the central and western regions. Specifically, the province with the highest per capita emissions shifted from Liaoning to Shanxi over the last two decades. Regarding the pace of change, per capita CO2 emissions increased the fastest between 2000 and 2010, slowing gradually thereafter  map for the spatiotemporal distribution of total COmissions , the hig2 emissions  were 0.523, 0.850, 0.652, 1.284, and 0.723, respectively, notably all of which were positive . Based o2 emissions in each province of China was significant. On average, for every 1% increase in total CO2 emissions within a given province, the total correlated CO2 emissions increased by 0.506%, 0.628%, 0.744%, 0.776%, and 0.504%, respectively. Accordingly, such growth in carbon emissions did not come from growth within the province, but rather was imported from neighboring provinces. Thus, carbon emissions in a particular province are not only highly correlated to its own internal factors, but closely related to neighboring factors  as well. Further, the spatial error terms in all five-time sections were significant at the α = 0.01 level, demonstrating that total CO2 emissions in China were influenced by coal, petroleum, natural gas, and non-fossil fuel energy sources, in addition to other factors.The spatial lag terms of the SEMLD in 2000, 2005, 2010, 2015, and 2019 were significant, indicating that the spatial spillover effect of total CO2 emissions, the results of the OLS residual test showed that the values of Moran’s I for the five years of 2000, 2005, 2010, 2015, and 2019 were 3.653, 1.560, 1.725, 2.261, and 0.435, respectively, with p-values of 2000, 2010, and 2015 being significant at the α = 0.10 level. These findings suggest a strong spatial autocorrelation among the residuals in this model, and confirm the presence of statistically significant spatial lag and error terms as well. When comparing the LogL, AIC, and SC values of the SLM, SEM, and SEMLD models, it was found that the SEMLD model had the greatest LogL value, and the smallest AIC and SC values, again indicating its stronger explanatory power  were all positive and exhibited a decreasing trend: 1.181, 1.175, 1.133, 1.129, and 0.607, respectively . Thus, i2 emissions per unit GDP in each province. Moreover, the spatial lag terms in this model were 0.825, 0.811, 0.654, and 0.874, respectively, all of which were significant at the α = 0.10 level. These findings suggest that on average, for every 1% increase in CO2 emissions per unit GDP within a given province, correlated CO2 emissions per unit GDP increased by 0.825%, 0.811%, 0.654%, and 0.874%, respectively.The spatial lag terms in the SEMLD for 2005, 2010, 2015, and 2019 were significant, indicating the importance of the spatial spillover effect of CO(1)The spatial distribution of the share of energy resources varies by energy source type. Coal use is ubiquitous, but peaks in central and western China, whereas petroleum use peaked in southern China, and natural gas in western and southwestern China. Alternatively, the epicenter of non-fossil fuel energy use was mainly situated in southwestern and southern China.(2)The change trends in the share of energy sources varied by type. The share of coal first increased, then subsequently decreased; whereas the share of petroleum decreased, increased, then decreased again. The share of natural gas showed an upward trend, while the same was also observed for that of non-fossil fuel energy resources.(3)The pace of change in the share of energy sources varied by type as well. Non-fossil energy consumption changed at the fastest pace, especially more recently; whereas petroleum and coal consumption changed more gradually. Alternatively, natural gas consumption changed at a moderate pace. Overall, there was relatively little change observed throughout China’s energy infrastructure due to the low base effect.(1)2 emissions, energy infrastructure essentially had the same impact on all three carbon emissions indicators. Coal has a net positive impact on the indicators, while the impacts of petroleum were positive at first, turning negative thereafter. Meanwhile, both natural gas and non-fossil fuel energy resources had a primarily negative impact on these indicators. The main reason behind these findings is that the amount of CO2 produced varies by energy source type per unit energy produced. Based on the ton of coal equivalent , carbon emissions from coal, petroleum, natural gas, and non-fossil energy were 2.50, 2.07, 1.61, and 0 carbon dioxide tons, respectively (without considering indirect carbon emissions caused by production processes). Hence, under the same conditions, provinces with higher shares of coal contributed more to CO2 emissions; whereas those with higher shares of natural gas and non-fossil fuel energy sources contributed less so.Despite the different trends of change in China’s total, per capita, and per unit GDP CO(2)2 emissions. Nevertheless, since the SEMLD had the greatest LogL value, in addition to the lowest AIC and SC values, it demonstrated the best goodness of fit among the three. Therefore, the results obtained using the SEMLD were chosen as the basis for correlation analysis in the present study.The statistical results of the three models, namely, the SLM (8), SEM (9), and SEMLD, were all significant, thus indicating that energy infrastructure is correlated with total, per capita, and per unit GDP CO(3)2 emissions, the strength of coal’s effects declined gradually over time, but remained the greatest of all energy source types. Alternatively, the impacts of petroleum on this indicator fluctuated, with an overall decreasing trend. Further, the impacts of natural gas on this indicator increased gradually with time, while those of non-fossil fuel energy sources fluctuated, with an overall upward trend. Regarding the impacts of energy structure on per capita CO2 emissions, coal fluctuated across a generally decreasing trend, while petroleum on this indicator declined at first, and increased thereafter. Conversely, the impacts of natural gas on this indicator rose gradually over time; whereas those of non-fossil fuel energy sources exhibited a continuously increasing trend, and were the greatest among all energy source types. Regarding the impact of energy structure on CO2 emissions per unit GDP, coal declined gradually over time, but was generally the greatest among all energy source types, while the impacts of petroleum fluctuated at first, before declining. Natural gas had an increasing impact on this indicator over time, and that of non-fossil fuel energy sources fluctuated across a generally downward trend. In summary, coal had the greatest impact on total and per unit GDP CO2 emissions, while non-fossil fuel energy sources and natural gas had the greatest impacts on per capita CO2 emissions.According to the SEMLD results, the impacts of energy infrastructure on carbon emissions varied widely over the analysis period, while varying effects on different carbon emissions indicators were observed as well. With respect to the impacts of energy infrastructure on total CO(4)Based on the spatial lag terms in the SEMLD, carbon emissions in a particular province were influenced not only by the energy infrastructure within, but also by the energy structure of neighboring provinces as well. On average, for every 1% increase in carbon emissions around a particular province, carbon emissions in the province increased by 0.5% to 0.9%. Hence, carbon emissions in neighboring provinces have a substantial impact on carbon emissions in a particular province.(5)According to the spatial error terms in the SEMLD, carbon emissions are not only significantly influenced by energy structure, but by other factors as well.After analyzing the characteristics of energy structure in China, and investigating the correlated impacts on carbon emissions, this study has put forth several suggestions for reducing carbon emissions within the energy sector, as listed below:(1)Energy transitioning is paramount to achieving carbon reduction; thus, China must set a clear direction toward energy transitioning in the future. For instance, natural gas and non-fossil fuel energy resources should be prioritized, followed by petroleum, while coal consumption should be phased out.(2)During the energy transition process, interactions between provinces should not be overlooked, as it remains necessary to strengthen energy exchanges between provinces for improving comprehensive energy efficiency. With the central and western regions’ rich hydro- and solar energy resources, as well as the coastal region’s abundant wind energy resources, electricity can be transmitted and distributed via the grid between regions to bolster the proportion of non-fossil fuel energy consumption in all neighboring provinces as well. In addition, international exchanges should also be strengthened .(3)Since carbon emission reductions are a complicated target, a series of effective measures in addition to energy transitioning can also be implemented, such as improving energy efficiency, and increasing carbon sink capacity.(1)This study did not explain the mechanism(s) behind the impacts of energy structure on carbon emissions, especially with respect to the variable strengths of impact observed. While the impacts of energy resources varied by type, the reasons behind such variations remained unexplored.(2)Although this study investigated the impact of China’s overall energy structure on carbon emissions based on provincial panel data from 1997 to 2019, these varied by province. Owing to the significant presence of spatial autocorrelation in carbon emissions, geographically weighted regression model analyses are essential, the results of which will be discussed in a forthcoming paper.(3)The present study confirmed the significant negative impacts of natural gas and petroleum on the reduction in carbon emissions; however, since both are high-carbon energy sources, there should be a boundary for such negative impacts, which has yet to be determined.There are several limitations with the present research, as listed below:(1)By analyzing energy flow under the whole life cycle, and energy exchanges between provinces, specific details of energy consumption can be identified to explore the specific factors driving the impacts of energy structure on carbon emissions.(2)The impacts of energy structure on carbon emissions in different provinces can be studied using geographically weighted regression models, so as to propose more targeted strategies for energy transitioning in each region.(3)Strengthen research on the cost of energy transition; the current renewable energy (zero carbon emissions) reserves are large, but are limited by technology and cost constraints, and cannot be quickly large-scale promotion, which is also the direction of future research ,45.(4)Strengthen the study of sectoral carbon emissions, combined with the analysis of sector-specific energy use and carbon emissions in the “China Energy Sector Carbon Neutral Roadmap” released by the International Energy Agency in September 2020; there is also a need to explore the relationship between energy structure and carbon emissions from different sectors in the future, especially key carbon-emitting sectors and enterprises, such as cement, iron and steel, chemical industry, transportation sector ,47, etc.Accordingly, this study proposes a few essential directions for future research:(1)China’s energy structure primarily consists of coal, petroleum, natural gas, nuclear, hydro, wind, and solar power. Notably, coal accounts for the highest proportion of the country’s energy infrastructure; however, its share has generally declined with time. Alternatively, petroleum constitutes the second-highest proportion of the country’s energy structure, but its share has also generally decreased with time. Non-fossil fuel energy resources maintain the third-largest share of energy infrastructure, and exhibit a rapidly increasing trend. Lastly, natural gas accounts for the lowest proportion of the country’s energy infrastructure; although, its share has been increasing steadily over time.(2)2 emissions, albeit at a slower pace in recent years. The epicenters of peak emissions have shifted from the eastern region to the central and western regions. Conversely, CO2 emissions per unit GDP have exhibited a continuous decreasing trend at a slightly slower pace, while its peak emissions have shifted in identical geographic directions.Since 1997, there has been a continuous increase in China’s total and per capita CO(3)2 emissions, with the high-high cluster being dominant.According to the results of the spatial autocorrelation analyses, there were significant spatial relationships between China’s total, per capita, and per unit GDP CO(4)2 emissions, while non-fossil fuel energy sources and natural gas maintained the greatest impact on CO2 emissions per unit GDP.The results of the spatial regression model showed that in China’s energy infrastructure, coal has a positive impact on carbon emissions; whereas natural gas and non-fossil fuel energy sources have a negative impact. The impact of petroleum on carbon emissions, however, turned from positive to negative over the analysis period. Simultaneously, the impact of energy sources on different carbon emissions indicators varied by energy source type, where coal had the greatest impact on total and per capita CO


Irisin is an adipo-myokine, mainly synthetized in skeletal muscles and adipose tissues, that is involved in multiple processes. Only a few studies have evaluated serum irisin in psoriatic patients. This study aims to analyze serum irisin levels in patients with chronic plaque psoriasis, to compare them with values in controls, and to assess whether concentration of circulating irisin correlates with the severity of psoriasis, calculated by means of Psoriasis Area and Severity Index (PASI). We enrolled 46 patients with chronic plaque psoriasis; the control group included 46 sex- and age-matched subjects without any skin or systemic diseases. Serum irisin levels were measured by competitive enzyme linked immunosorbent assay. Our results showed a non-significant increase in serum irisin concentration in psoriatic patients compared to controls. A negative non-linear correlation between PASI and irisin levels was detected in psoriatic patients. Indeed, dividing patients according to psoriasis severity, the negative association between irisin and PASI was stronger in patients with mild psoriasis than in patients with higher PASI scores. Several control variables we tested showed no significant impact on serum irisin. However, erythrocyte sedimentation rate in the normal range was associated with significantly higher irisin levels in psoriatic patients. In conclusion, although irisin levels were not significantly different between controls and psoriatic patients, irisin was found to be negatively associated with psoriasis severity, especially in subjects with low PASI scores; however, further studies are needed to clarify the role of irisin in subjects with psoriasis. Psoriasis is a common chronic immune-mediated inflammatory disease with a prevalence ranging from 0.73% to 2.90% in Europe . The mosRecent evidence suggests that psoriasis should be regarded as a systemic inflammatory disease and not simply a disease limited to the skin ,4. PatieIrisin is a 112 amino acid-adipomyokine produced by the proteolytic cleavage of the transmembrane glycoprotein named fibronectin type III domain-containing protein 5, as described in 2012 by Boström et al. .After its identification, irisin was extensively studied and has been implicated in multiple processes, including white adipose tissue browning, regulation of energy expenditure, bone metabolism, and glucidic and lipidic homeostasis, and it was also shown to exert anti-inflammatory and neuroprotective effects ,14,15,16Only a few studies have evaluated serum irisin in patients with psoriasis ,28,29,30The study was approved by the Bioethical Committee of the University of Bari, Italy.Eligibility criteria of cases were: age ≥ 18 years; diagnosis of chronic plaque psoriasis; signed informed consent; absence of history of malignancies; and no use of any systemic or topical drugs for treatment of psoriasis for at least one month prior to enrolment. For each patient the following data were recorded: demographic  and anthropometric  characteristics, PASI, psoriasis duration, history of PsA, and cardio-metabolic comorbidities . In addition, we retrieved information about other comorbidities, which were included in a residual category.The control group consisted of sex- and age-matched subjects who did not have any cutaneous or systemic acute or chronic diseases.For all patients, blood samples were collected to determine serum levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and irisin. Irisin serum concentrations were measured in cases and controls by competitive enzyme linked immunosorbent assay , with a detection limit of 0.001 mcg/mL, intra-assay coefficient of variability (CV) of 6.9% and inter-assay CV of 9.07%. Notwithstanding challenges in the measurement of serum irisin levels ,23, we aAll data were manually entered into an Excel spreadsheet and then analyzed by means of the R Statistical Software (version 4.0.3). Continuous variables were described through their mean, standard deviation (SD), median, interquartile range, minimum and maximum. Categorical variables were summarized by their shares in the sample. The Mann–Whitney test was used to detect serum irisin difference between psoriatic patients (cases) and controls.In order to detect robust associations between irisin levels and PASI scores among psoriatic patients, we performed Spearman correlation and a series of ordinary least squares (OLS) regressions, accounting for a number of potential confounding factors .p-values less than 0.05 were considered statistically significant. As a non-linear relationship between severity of psoriasis and irisin levels was suspected, the sample of psoriatic patients was divided into three groups depending on the severity defined by the PASI score. In particular, PASI values < 10 were classified as low (mild psoriasis), between 10 and 20 as moderate (moderate psoriasis) and >20 as high (severe psoriasis) [Taking into consideration the sample size of the study, oriasis) .ESR was regarded as a dichotomous variable, coded 1 if it was within the normal reference range and 0 if abnormal.2 (SD 6.25) and the mean PASI score was 13.3 (SD 9.57). Patients with mild psoriasis (39% of the sample) had an average PASI score of 4.66 ± 2.41, those with moderate psoriasis (37% of the sample) had a mean PASI value of 13.7 ± 3.15, whereas the mean PASI value in severe cases (24% of the sample) was 28.1 ± 5.11. The following comorbidities were recorded: PsA (26.3% of patients), arterial hypertension (17.4%), type 2 diabetes (6.5%), dyslipidemia (6.5%) and others (30.4%).Forty-six patients  with chronic plaque psoriasis, recruited between September 2016 and February 2018 at the Dermatology Unit of University of Bari, in Southern Italy, participated in this study. None of the patients had previously received treatment with biologics for psoriasis and/or PsA. The mean age of patients was 52.5 years (SD 15.9), the average BMI was 27.0 kg/mThe control group was composed of 46 sex- and age-matched individuals without any cutaneous or systemic diseases.p = 0.201).Irisin levels in patients were marginally higher than in the control group . In partp = 0.041)  .p = 0.01), and versus patients with moderate psoriasis (p = 0.04).Comparing median irisin levels in each of the three groups (stratified on the basis of PASI scores) with levels in control subjects, we found that irisin was higher in patients with mild psoriasis compared to controls and to patients with more severe psoriasis . The ManMoreover, we noticed that the intensity of the irisin-PASI relationship diminished, moving from the group with mild psoriasis to the group with severe psoriasis .2) to account for a non-linear relationship.Finally, we performed a multivariate analysis to investigate which factors affect the relationship between irisin levels (in log) and PASI . We estip = 0.455) in the full sample, while we found a negative yet non-linear correlation , while no significant relationship was detected for CRP (data not shown).In the multivariate analysis, we also investigated the influence of two reliable markers of systemic inflammation, ESR and CRP, on serum irisin. Our results indicated that irisin levels were 45% higher in psoriatic patients with an ESR score within the normal range compared with patients who had elevated ESR values . Spearman’s correlation showed a significant negative correlation between irisin and PASI in patients with psoriasis, but this correlation was lost in the multivariate analysis. However, by dividing patients according to their psoriasis severity, we reported a heterogeneous significantly negative association within the PASI groups, indicating a non-linear relationship between the two parameters. Interestingly, our patients with higher PASI scores also had higher BMI values compared to patients with milder forms of psoriasis (data not shown), suggesting that metabolic alterations or mechanisms related to increased adipose tissue might contribute to determine irisin values in such a group of patients. Indeed, a negative and non-significant linear correlation was observed between serum irisin and BMI when considering the full sample of patients , in lineFurthermore, we found that irisin in psoriatic patients did not significantly correlate with both the duration of the disease, in contrast to a previous study , and theIndeed, prior assessments of psoriasis patients showed no statistically significant correlations of serum irisin levels with glucose level ,28,30, u2 were independent predictors of a higher irisin level. On the contrary, in the study carried out by Bulur et al. [In addition, Baran et al. noticed a significant positive correlation between irisin in psoriasis patients and laboratory parameters of inflammation, namely CRP and ESR, as well as lipocalin-2 , a proter et al. , there wSimilarly, the results of a meta-analysis showed no significant correlation between circulating CRP and irisin levels, although a significant positive correlation was detected in overweight or obese subjects, and subgroup analyses revealed significant, positive, but weak correlations in cohort studies, studies involving healthy people, and studies with a male-to-female ratio < 1 .Some studies have highlighted that irisin might have a role in immunity, as well as anti-inflammatory and anti-oxidative effects ,33. MoreOverall, our findings are in contrast with some previous evidence in the literature. In general, inconsistencies might be related, at least partially, to the limited size of patient samples and different eligibility criteria of patients enrolled in such studies. It should be emphasized that the measurement of circulating irisin is still challenging because of the questionable accuracy of the available detection methods ,23.The main limitation of our study reflects a relatively small population involved in the analysis and the omission of further inflammatory stimuli in psoriatic patients that we cannot control for. For example, we collected anamnestic data expressing only the presence or absence of PsA, without a more precise quantification of its severity and activity. Furthermore, the presence of comorbidities or physiological conditions capable of interfering with irisin serum levels was not systematically assessed and was not generally regarded as an exclusion criterion for the recruitment of psoriasis patients in our study. Several factors, such as physical activity, diet, hormonal conditions, metabolic diseases and various other pathological disorders , as well as treatments for such conditions, may affect the circulating irisin levels ,16.However, our results are robust to a variety of control variables and model specifications. The stability of our quantitative estimates across different regression models , obtaining contrasting results, in partial opposition with some previous studies. Overall, our results suggest that irisin concentration in psoriatic patients displays a complex pattern, which still needs to be elucidated. Additional studies are necessary to understand the possible role of irisin in psoriasis and uncover the relationship between irisin levels and the severity of psoriasis.Noteworthy, it would be welcome to examine the role of irisin within larger groups of patients with psoriasis and other inflammatory skin diseases in order to distinguish between those mechanisms that pertain just to psoriasis and those that have a more general validity.


To compare the safety and efficacy of endoscopic retrograde cholangiopancreatography (ERCP) and percutaneous transhepatic cholangial drainage (PTCD) in the treatment of malignant obstructive jaundice, a systematic review and meta-analysis of published studies was undertaken to assess the differences between the two procedures in terms of efficacy and safety. From November 2000 to November 2022, the Embase, PubMed, MEDLINE, and Cochrane databases were searched for randomized controlled trials (RCTs) on the treatment of malignant obstructive jaundice with ERCP or PTCD. Two investigators independently assessed the quality of the included studies and extracted the data. Six RCTs, including 407 patients, were included. The results of the meta-analysis showed that the overall technical success rate in the ERCP group was significantly lower than that in the PTCD group ), but with a higher overall procedure-related complication incidence rate ). The incidence of procedure-related pancreatitis in the ERCP group was higher than that in the PTCD group ), and the differences were statistically significant. No significant difference was observed between the two groups when the clinical efficacy, postoperative cholangitis, and bleeding rate were compared.Both treatments for malignant obstructive jaundice were efficacious and safe. However, the PTCD group had a greater technique success rate and a lower incidence of postoperative pancreatitis.The present meta-analysis has been registered in PROSPERO Malignant obstructive jaundice (MOJ) can lead to pathophysiological disorders of multiple organ systems throughout the body, including systemic electrolyte imbalance, immune system injury, coagulation disorders, digestive system insufficiency, and malnutrition. If the obstruction cannot be removed in time, it may cause biliary infection, liver and kidney failure, and even death , 2. MostThere are many different types of biliary drainage operations in clinical practice, among which two types of procedures are prevalent: 1. Endoscopic retrograde cholangiopancreatography (ERCP): The endoscope is inserted into the descending part of the duodenum through the duodenal papilla into the bile duct, with the biliary stent placed through the site of the obstruction. ERCP, an effective treatment for obstructive jaundice, drains bile into the body or intestinal tract, quickly drains bile to relieve biliary obstruction and compression, removes jaundice, and improves liver function. 2. Percutaneous transhepatic cholangial drainage (PTCD): This procedure involves inserting an internal or external drainage cannula into the dilated bile duct through the liver under the guidance of X-ray or ultrasound to quickly discharge bile and ameliorate jaundice. With the continuous progression of endoscopic and percutaneous drainage, these procedures have gradually become the most effective methods known to alleviate MOJ; they can effectively reduce bilirubin levels in the blood, improve liver function, improve nutritional status, prolong life expectancy, and thus improve the quality of life, especially for obstructive jaundice with unresectable tumors. Therefore, ERCP or PTCD has become the initial treatment for obstructive jaundice, but the optimal treatment remains controversial.In this study, we aimed to compare the differences in the technique success rate, clinical efficiency, and incidence of postoperative complications between the two methods through evidence-based medical analysis to evaluate the advantages and disadvantages of the two methods in the treatment of MOJ and to explore the best BD method for patients with MOJ.2Based on the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  and Coch2.1From November 2000 to November 2022, randomized controlled trials on the treatment of malignant obstructive jaundice with ERCP or PTCD were searched in the EMBASE, PubMed, MEDLINE, and Cochrane databases using the same index terms “ERCP, PTCD, PTBD, MOJ; endoscopic retrograde cholangiopancreatography, percutaneous transhepatic cholangial drainage, malignant obstructive jaundice”. The included literature had to be randomized controlled trials. Retrospective controlled trials, unpublished literature, case reports, and reviews were also excluded. Two researchers reviewed all of the literature and abstracts according to the study’s requirements, excluding unqualified literature and reading the full text of any literature that could potentially be included to determine whether it met the inclusion criteria. All disagreements were resolved by discussion.2.22.2.1via imaging data as biliary stricture or occlusion caused by a primary or metastatic malignant tumor, such as pancreatic cancer, hilar cholangiocarcinoma, ampullary carcinoma, and other tumors. The patients were informed and agreed to participate in the study and provided written informed consent. Intervention measures in the experimental group: ERCP was used to treat malignant obstructive jaundice. The control group was treated with PTCD.All included investigations were English studies comparing PTCD and ERCP for malignant biliary obstruction. Subjects: Malignant obstructive jaundice is typically clinically diagnosed 2.2.2Studies were excluded if they were nonrandomized controlled studies, incomplete randomized controlled studies, retrospective analysis studies, conference abstracts, complete texts without original data, duplicate reporting studies, letters, or review styles.2.3Data on the publication year, authors, number of subjects, methodological characteristics, and evaluation indices  were extracted. The bias risk assessment tool provided by the Cochrane Library was used to assess the quality of randomized controlled trials by two researchers independently, including the method of random allocation and whether subjects and study implementers and measurement results were blinded. The tool also assesses whether the data are complete and selective reporting of research results and other possible sources of bias. A consensus was reached after discussion when a controversy arose. Otherwise, divergence was resolved by third parties.2.4The extracted data were statistically analyzed using the software package Rev Man 5.3. To compare outcomes, the odds ratio (OR) and mean difference (MD) were calculated as effect sizes for dichotomous and continuous variables, respectively, including their combined value and 95% confidence interval (95% CI). A χ2 test was conducted to examine the heterogeneity among the included studies using the inconsistency index (I2) statistic. Heterogeneity was identified as P>0.10, I2>50%, in which a random-effects model was used; otherwise, the fixed-effects model was used for homogeneity, and two-sided P<0.05 was considered statistically significant.33.1The search strategy identified 1432 articles, of which 154 duplicate articles were excluded, 1256 irrelevant articles were excluded after reading the titles and abstracts, and 22 articles remained initially. Full texts were assessed for eligibility . Finally, seven articles –13 were 3.2The overall technical success rate was reported in all six articles, and there was no heterogeneity among the outcomes; therefore, a statistical analysis was conducted using the fixed effect model. The results of the meta-analysis: Z=3.19, P=0.001, OR=0.31 (95% CI: 0.15-0.64). The difference was statistically significant, and the total success rate of surgery in the PTCD group was significantly higher than that in the ERCP group Figure 33.3The total clinical efficacy was reported in six studies, and heterogeneity was observed among the results of each study. The random-effects model was applied, and the results of the meta-analysis were as follows: Z=1.76, P=0.08, OR=0.46 (95% CI: 0.20-1.09), indicating that the difference was not statistically significant, and there was no significant difference in total clinical efficacy between the ERCP and PTCD groups Figure 43.4The incidence of overall procedure-related complications was described in six studies, and there was no heterogeneity among the results of each study. Statistical analysis was conducted using the fixed-effect model, and the results of the meta-analysis were as follows: Z=2.57, P=0.01, OR=0.55 (95% CI: 0.34-0.87), indicating that there was a significant difference in the total complication rate between the two groups, with the PTCD group having a higher overall complication incidence Figure 53.5The incidence of postoperative cholangitis was reported in all six articles, and there was heterogeneity among the results; thus, the random-effects model was used for statistical analysis. The results of the meta-analysis revealed Z=0.21, P=0.83, OR=0.87 (95% CI: 0.24-3.16), and there was no significant difference in the incidence of postoperative cholangitis between the ERCP and PTCD groups Figure 63.6Procedure-related pancreatitis was reported in all six articles, and there was no heterogeneity among the results; therefore, statistical analysis was conducted using the fixed-effect model. The results of the meta-analysis were as follows: Z=2.80, P=0.005, OR=5.29 (95% CI: 1.65-16.97). The difference was statistically significant, and the incidence of postoperative pancreatitis in the ERCP group was significantly higher than that in the PTCD group Figure 73.7There was no heterogeneity among the results; therefore, a fixed-effects model was used for statistical analysis. The results of meta-analysis: Z=1.90, P=0.26, OR=0.54 (95% CI: 0.19-1.58). The difference was statistically significant, and there was no significant difference in the postoperative bleeding rate between the ERCP and PTCD groups Figure 83.8Publication bias analysis based on a funnel plot of technique success. No publication bias was detected with the observed indicators Figure 93.92 = 0), showing that the sensitivity of the included literature was low and that the results of this analysis were stable.Sensitivity analysis is a crucial component of meta-analysis because it determines the overall credibility of the observed results. The results can be considered reliable if they remain consistent across sensitivity analyses. A meta-analysis of the remaining studies was conducted to assess the stability of the results. Individual investigations were eliminated item by item using a sensitivity analysis. After excluding each study and reintegrating the effect values, all were within the CI. There was no significant difference before removal (I4in situ and provides rapid relief of symptoms. Most patients with obstructive jaundice are treated with PTCD. Although the effect was significant and the prognosis could be improved, the incidence of postprocedural complications was still relatively higher (The methods of biliary drainage have been changing with the advancement of endoscopic technology, and PTCD became a prevalent technique in the late 1980s owing to its milder trauma, fewer comprehensive indications, and better economic benefits. PTCD helps restore physiological continuity to the biliary tract y higher , 15. Wity higher , 16. Howy higher .In contrast, bile outflow may negatively affect digestive and liver functions. Therefore, some guidelines recommend ERCP as the preferred treatment for malignant obstructive jaundice . ERCP is4.1This meta-analysis favors PTCD over ERCP for achieving satisfactory technical success as initial treatment in patients with MOJ. Otherwise, the two treatments had the same effectiveness in biliary drainage. A study  reported4.2In this meta-analysis, ERCP was associated with fewer overall postprocedural adverse events and more procedure-related pancreatitis than PTCD, which is considered a prognostic factor in patients and a reference strategy in the management of MOJ. Mild complications affect the clinical efficacy in patients, while serious complications may cause disease progression or even lead to the death of patients. The mortality rates associated with ERCP and PTCD have been reported to be 0.1% and 2%, respectively , 24. In 4.4This is the first meta-analysis to compare the efficacy and safety of ERCP with PTCD management of biliary obstruction based on definite RCTs. We systematically evaluated the short-term efficacy and safety of ERCP and PTCD for malignant obstructive jaundice in 6 RCT studies. However, there are still some shortcomings because the inherent limitations of the meta-analysis and the included studies may have weakened our analysis. We could not evaluate the long-term efficacy and safety because such data on 30-day mortality were only provided in one paper. In addition, due to such limitations, we could not analyze the efficacy and safety of different types of procedures. Additionally, there was heterogeneity in a few observation indicators in this study, attributed to the technical variance of operators in different institutions and long time spans, and our comparative analysis of specific complication rates and mortality was limited by the small sample size. Despite these limitations, we believe that our assessment is reliable for comparing the effectiveness and safety of the two methods.5Based on the available information and the acknowledged limitations of the datasets included in the present study, which incorporated data from 6 RCT studies that included more than 407 patients, the results of this meta-analysis suggest that PTCD is associated with more procedure-related and postoperative complications than ERCP. With regard to similar clinical efficacy, we recommend ERCP as the initial decompression of malignant biliary obstruction. In addition, both methods are technically demanding operations, and we recommend that unskilled surgeons perform them under supervision to ensure clinical safety.CB and JX performed the search and drafted the manuscript. YF and HH performed the data extraction and analyzed the data. ZZ and QX designed the study and amended the original draft. All authors contributed to the article and approved the submitted version.


Renal Cell Carcinoma (RCC) encompasses three histological subtypes, including clear cell RCC (KIRC), papillary RCC (KIRP), and chromophobe RCC (KICH) each of which has different clinical courses, genetic/epigenetic drivers, and therapeutic responses. This study aimed to identify the significant mRNAs and microRNA panels involved in the pathogenesis of RCC subtypes. The mRNA and microRNA transcripts profile were obtained from The Cancer Genome Atlas (TCGA), which were included 611 ccRCC patients, 321 pRCC patients, and 89 chRCC patients for mRNA data and 616 patients in the ccRCC subtype, 326 patients in the pRCC subtype, and 91 patients in the chRCC for miRNA data, respectively. To identify mRNAs and miRNAs, feature selection based on filter and graph algorithms was applied. Then, a deep model was used to classify the subtypes of the RCC. Finally, an association rule mining algorithm was used to disclose features with significant roles to trigger molecular mechanisms to cause RCC subtypes. Panels of 77 mRNAs and 73 miRNAs could discriminate the KIRC, KIRP, and KICH subtypes from each other with 92%  and 95% accuracy , respectively. The Association Rule Mining analysis could identify miR-28 (repeat count = 2642) and CSN7A (repeat count = 5794) along with the miR-125a (repeat count = 2591) and NMD3 (repeat count = 2306) with the highest repeat counts, in the KIRC and KIRP rules, respectively. This study found new panels of mRNAs and miRNAs to distinguish among RCC subtypes, which were able to provide new insights into the underlying responsible mechanisms for the initiation and progression of KIRC and KIRP. The proposed mRNA and miRNA panels have a high potential to be as biomarkers of RCC subtypes and should be examined in future clinical studies. RCC comprises 3% of all malignant neoplastic cases in adults and approximately 90% of malignant kidney tumors2. Based on the American Cancer Society estimation, 1 in 46 men and 1 in 80 women will be diagnosed with RCC during their life. Also, the 5-year survival rate of patients with RCC is 73.7%3. Smoking, obesity, and hypertension are important risk factors that can affect RCC occurance4. The incident and mortality rate of RCC highlight the importance of screening programs to develop reliable biomarkers for early detection of its subtypes5.Renal Cell Carcinoma (RCC) possesses the 15th rank among frequent cancers, according to the GLOBOCAN report6. KIRC is identified by mutations in the VHL (von Hippel–Lindau) gene and the loss of chromosome 3p. The clinical severity of KIRC is higher compared with KIRP and KICH subtypes 7 due to the lack of effective biomarkers for earlier detection. In the early stages, KIRC is frequently asymptomatic and 25–30% of patients are often diagnosed at metastasis status; hence, presenting a high mortality rate8. KIRP is characterized by the loss of chromosome 9p and trisomy of chromosomes. In KIRP, a few subgroups of patients have a satisfactory treatment outcome, while a wide range of patients needs to develop promising treatment strategies10. Small incidentally detected kidney masses have a major diagnostic dilemma since a section of them can be benign and managed conservatively11. A lower-risk RCC, KICH, is identified by the loss of chromosomes and can pose a slight risk to a patient if cautiously treated with routine surveillance rather than surgery11. KICH has enormous potential to be diagnosed earlier compared to other RCC subtypes due to its variable long-term outcome12. Due to these distinct clinical and biological behaviors, differentiating and accurate detection of RCC subtypes via non-invasive and precise biomarkers confidently help physicians perform an appropriate therapeutic decision. This could be different from total and partial nephrectomy or even close follow-ups.The histological subtypes of renal cancer include; clear-cell RCC , papillary RCC , chromophobe RCC , and other rare subtypes (< 1%)16 and none of them can be employed in clinical application17. Furthermore, computed tomography (CT) and abdominal ultrasound have problems including high costs and low sensitivity in the diagnosis of small tumors18. Thus, the development of non-invasive and accurate screening for different types of RCC is a necessity. Comprehensive understanding of the molecular mechanisms of RCC subtypes is the main challenge in RCC research to identify novel and reliable molecular biomarkers.It has been shown that candidate biomarkers are not reliable for the diagnosis and prognosis of different RCC subtypesThis study was designed to introduce panels of mRNAs and miRNAs for discriminating different subtypes of RCC and detect remarkable molecules that have significant pathological roles in RCC subtypes through machine learning and artificial intelligence approaches. Firstly, we implemented reading and pre-processing of the RNA-sequencing data. Next, to identify candidate features (mRNAs and miRNAs), we applied feature selection based on filter and graph algorithms. Then, to evaluate selected candidate features, we employed a deep learning model to classify the subtypes of RCC. Finally, an association rule mining algorithm was used for detecting remarkable features which play significant roles in firing molecular mechanisms to cause RCC subtypes.https://portal.gdc.cancer.org) provided by The Cancer Genome Atlas (TCGA) dataset19. Downloaded data were utilized to study the molecular mechanism of RCC subtypes, including ccRCC (KIRC), pRCC (KIRP), and chRCC (KICH). In the TCGA renal cell carcinoma project, 60,482 mRNAs expression and 1,881 miRNAs expression were reported for each patient. mRNA expression and clinical data were provided for 611 ccRCC patients, 321 pRCC patients, and 89 chRCC patients. Moreover, miRNA expression and clinical data were reported for 616 patients in the ccRCC subtype, 326 patients in the pRCC subtype, and 91 patients in the chRCC subtype. Information on each subtype is presented in Table In this study, the renal cell carcinoma data, including mRNA, miRNA, and clinical data were downloaded from the GDC portal ,,2), \doc20. Therefore, a suitable subset of candidate features was identified using the proposed algorithms. In miRNA data, a graph-based algorithm extracted the suitable subset of candidate features among 1545 features. However, in mRNA data, we first utilized the filter-based method for primary feature selection due to the existence of 58,226 features that could cause a high computational cost in the graph-based algorithm. Thus, the filter-based approach reduced the mRNA data dimension from 58,226 to 1000, and a graph-based algorithm extracted the suitable subset of candidate features among 1000 features. In the following, the proposed algorithms are specified in more detail.In feature selection, we applied a new graph-based method for feature selection in mRNA and miRNA data separately21. In this regard, we used the 22. The Filter-based methods interpret the feature selection process by calculating importance measures for each feature separately. These algorithms do not apply any evaluation tool such as a classifier and are called classifier-independent techniques. Filter-based methods have low computational and time costs; however, they do not consider features interactions in the feature selection process. In this sub-step, we applied the filter-based method for primary feature selection in mRNA data. Filter-based method help to remove some irrelevant features and reduce the dimensionality for use in the graph-based process. Many filter methods have been introduced and utilized in various data with different domains, but a few are suitable for high dimension and low sample size (HDLSS) datad by Eq. .3\documeth feature has low relevancy with a defined phenotype.In Eq. , the \do in Eqs.  and 5),,\documenth feature contains zero among reported values, then If the id on Eq.  and \doc23. These methods display the search space as a graph using a graph representation of features. Then the principles of graph theory are employed for selecting the most relevant attributes. The graph-based method contains three sub-steps, including graph representation of feature space, community detection/graph clustering, and selecting the significant nodes in each cluster.Recently, graph-based methods are used for feature selectionIn the first phase, the feature set is mapped to the graph space. The graph is defined by n in Eq. .7\docume20, we applied the ure [Eq. ], \documn in Eq. . We empl24. In addition, the Louvain algorithm is simple in the implementation phase.In the second phase, we applied a community detection algorithm to cluster graph space. The community detection algorithm performs clustering by finding groups of nodes that have high-density connectors internally. Nodes in the same community have high similarity properties. Community detection assists in better understanding sophisticated networks, such as genomic data networks. In recent years, researchers have introduced various community detection algorithms. Louvain algorithm is one of the fastest among them. Louvain algorithm performs the clustering process by maximizing the modularity objective function, in which the quality of partitions is compared, by the community detection processIn a graph with n nodes, the Louvain algorithm starts firstly with n communities by allocating each node to one community. The algorithm works based on a random selection of a node and transferring from its community to another one, then the gain modularity is calculated. This process is repeated until no improvement happens, and the algorithm will finish.25 to get the MIS in each community/cluster. Also, we defined the adjacency matrix to employ in the R. Boppana algorithm. It is a Boolean graph matrix that is calculated by Eqs.  concept based on graph theory is utilized for node selection in subgraphs. A subset of the vertex set of a graph is independent if and only if it includes no pair of adjacent vertices. Identifying the independent set with the maximum size is an NP-hard optimization problem. It is doubtful that there exists an efficient algorithm for finding an MIS of a graph. In this study, we applied the proposed algorithm of R. Boppana et al. based on by Eqs.  and 12)25 to getIn this step, we applied a classifier to evaluate the candidate features selected in the previous step. High accuracy (or any user-defined measure) of classification can mark the success of the feature selection method in choosing the relevant attributes. Otherwise feature selection method cannot identify relevant features.26. The description details of the self-organizing deep auto-encoder are available in 1-score, and AUC-ROC were applied to evaluate the classifier performance, as shown in Eqs. .13\.13\26. TAssociation Rule Mining can extract efficient associations among data items. An association rule is defined in n in Eq. . SupportIn Eq. , \documen in Eq. . ConfideThe Lift of rule n in Eq. .19\docum27. In recent years many researchers have proposed many improved algorithms, such as FP-Growth29, Apriori-Hybrid30, Fuzzy association rule31, etc.Association rule mining mainly contains two phases: Frequent itemset generation and Rule generation. In the Frequent itemset generation, the algorithm generates all itemsets iteratively, then itemset that its support count is more than the min_support (user-defined threshold) reported as a frequent itemset. In the rule generation, association rules are made based on frequent itemsets. Agrawal et al. introduced the Apriori algorithm as one of the first association analysis algorithms, in which the property of frequent itemset is applied successfully by R. AgrawalIn this study, we utilized the FP-Growth algorithm for association analysis. In terms of computational, storage space, and time, the FP-Growth is one of the best algorithms for association analysis due to one-time searching itemset space. The pseudo-code of the FP-Growth algorithm is shown in Tables We executed the following steps to the miRNA and mRNA data Fig. . In the In mRNA data, first, we used the filter method based on the mentclass2pt{minim1-score, and AUC of mRNA and miRNA data are reported individually in Table We applied the classifier to evaluate the discrimination power of candidate features (mRNAs/miRNAs) among RCC subtypes. In this regard, the self-organizing deep auto-encoder was utilized for performing classification tasks. The accuracy, FIn similar studies, all subtypes, including ccRCC, pRCC, chRCC, WT (Wilms tumor), and RT (Rhabdoid tumor), were classified based on selected miRNAs in the feature selection process. However, these subtypes are different in cancer nature and patient type. The ccRCC, pRCC, and chRCC are common adult kidney cancer. In contrast, WT and RT are common pediatric kidney cancer. Thus, feature selection and classification of all subtypes may lead to missing information related to pediatric kidney cancer subtypes. In addition, the comparison classification accuracy of similar studies with the proposed method will be difficult due to this difference. Nevertheless, the accuracy of the applied methods is illustrated in Table In the association rule mining process, candidate features were scaled into the [0 1] range using min–max normalization. Next, candidate features (mRNAs/miRNAs) were discretized into three categories, including low, medium, and high levels, so that the number of miRNA and mRNA items were equal to 212 and 233, respectively. Then, the FP-Growth algorithm was applied to generate frequent itemsets and association rules. Parameters of the algorithm, including min-support (frequent itemset), max-length (maximum length of frequent itemset), and lift (association rule), were set to 0.1, 4, and 1.1, sequentially.To discover patterns, association rules that consequently were equal to KIRC/KIRP/KICH were selected. In the miRNA data, the number of association rules related to KIRC/KIRP was equal to 27,635/23,198. Moreover, in the mRNA data, the number of association rules related to KIRC/KIRP was equal to 94,354/28,956. Due to the lack of samples in the KICH subtype, frequent itemsets and related association rules were not generated; the support count of itemsets was less than the min-support threshold. In the antecedent part of the selected association rules, mRNAs and miRNAs with the highest repeat count were considered significant features in each RCC subtype. In Figs. Box plots of five top miRNAs and mRNAs in KIRC and KIRP subtypes are illustrated in Fig. CSN7A (ENSG00000111652.8) and NMD3 (ENSG00000169251.11) were the most frequent itemset with 5774 and 2306 repeat counts in KIRC and KIRP association rules, respectively. Moreover, miR-28 and miR-125a were the most frequent itemset with 2642 and 2591 repeat counts in KIRC and KIRP association rules, respectively. As a result, we decided to examine these miRNAs and mRNAs more closely using other association rules to investigate their relation with another feature. More in-depth coverage of these findings is available in the discussion section of the study. In this regard, we showed these relations based on association rules in the graph network  that determine the substrate-selectivity in response to specific stimuli either to degrade by the proteasome or modify their function. The COP9 signalosome (CSN), another component of the UPS, is a multi-subunit (CSN 1–8) metalloprotease complex37 that plays roles in gene expression, cell-cycle control, and DNA-damage response38, and increases the stability of some proteins including EGFR39. Furthermore, the CSN plays a role in controlling the activity of NF-κB, an inflammatory transcriptional regulator involved in cell survival, proliferation, and transformation41.The CSN7A, UBAC1, PSMD9, RNF40, and Capn4 were identified as candida mRNAs in the KIRC with the highest repeat counts by the association rule mining analysis. Machine learning approaches could detect novel targets in the field of the KIRC and bring the UPS (ubiquitin–proteasome system) and its components  into the focus of interest. The degradation of regulatory proteins by the UPS has an important function in controlling the cell cycle progression, DNA repair, response to extracellular stress, and signal transduction. The ubiquitin, proteasome, ubiquitinating enzymes (including ubiquitin-activating (E1), -conjugating (E2s), and -ligases (E3s) enzymes) along with deubiquitinating enzymes are the key components of the UPS system42. Under the loss of the VHL, the degradation of its specific targets, hypoxia inducible factors (HIF1α and HIF2α) does not occur, making a pseudohypoxia state. Both VHL deficiency and the accumulation of the HIF-1 promote the NF-κB activity that subsequently stimulates an NF-κB/PI3K/AKT/TGF-β/EGFR/IKK signaling cascade, resulting in the activation of proliferation and glycolytic pathways, a pro-angiogenic and apoptosis-resistant phenotype, and highly vascularized tumors44. It is reported that the CSN elevates the efficacy of the VHL-mediated HIF-1α recognizing, ubiquitination, and degradation45.The VHL is the substrate recognition subunit of the E3 ubiquitin ligase complex that polyubiquitylates the hydroxylated targets under normoxia46 and the transcription-coupled nucleotide excision DNA repair47. Moreover, an overexpressed CSN7A could stimulate the IκBα deubiquitinylation and; consequently, suppressing the transcriptional activity of the NF-κB48. The mechanism by which the CSN7A can impact KIRC has not been defined; however, the CSN7A may function alike in the KIRC.In our deep learning analysis, the CSN7A (COPS7A), 7a subunit of the CSN, was identified as the first top mRNA with the highest role in the KIRC. It is reported that in tumor tissues, the CSN7A level is decreased which may be associated with the oxidative phosphorylation pathway50. The UBAC1 also contributes to the inflammatory signal transduction pathways and affects cell proliferation and viability in keratinocytes51. The role of the UBAC1 in the KIRC needs to be clarified. The PSMD9 was 3rd top KIRC-related mRNA found in our rule mining analysis. The PSMD9, as a part of the 26S proteasome, regulates protein degradation52. The PSMD9 is overexpressed in tumor tissues and associated with cell proliferation, hostile tumor outcome, and resistance to the therapy55. Since different tumor suppressors and oncogenes are controlled by the Ub- and proteosome-mediated degradation, the CSN7A, UBAC1, PSMD9, and RNF40 may play important roles in the pathogenesis of the KIRC. The CSN7A in the KIRC association rules has a high dependency on PSMD9, CAPNS1, and UBAC1, SNU13, respectively. Our study may open an innovative horizon to investigate the role of the CSN7A, UBAC1, PSMD9, and RNF40 in the pathogenesis of the KIRC.The UBAC1, the second identified mRNA in this study, is a subunit of the KPC complex, an E3 ubiquitin-protein ligase. The UBAC1 acts with the proteasome and ubiquitinated proteins such as the Nf-κBmicroRNAs are critical managers of the development and progression of the RCC. They function as oncomirs or anti-oncomirs. miR-28, let-7i, miR-23b, miR-125a, miR-22 were top five identified miRNAs that were dysregulated in KIRC. For more details see In the Rule mining analysis, the NMD3, ZNF41, CFAP36, FGFR1OP2, and RGL1 were identified as the top five mRNA with high association rules in the KIRP (≥ 2195).56 and their induced abnormal translation can provoke a malignant phenotype independent of chromatin remodeling and the deregulation of the transcriptional process57. The 60S ribosomal export protein NMD3 (NMD3) was the first identified mRNA involved in the pathogenesis of the KIRP in our study. The NMD3 is a nuclear adaptor protein that transports the 60S subunit into the cytoplasm via the nuclear pore complex and participates in the cytoplasmic maturation of 60S particles59. In the cytoplasm, the release of the NMD3p from 60S subunits needs a GTPase and the ribosomal protein (Rpl10p). Any mutation in these proteins leads to cytoplasmic retention of the NMD3 on pre-60S subunits, blocking ribosome assembly and biogenesis61. The NMD3 also exerts a significant effect on RNA biosynthesis, mainly ribosomal RNA, and consequently may impact tumorigenesis62. A direct role of the NMD3 needs to be elucidated in the KIRP.The transcript patterns of the ribosomal proteins are tumor- and tissue-specificRespectively, miR-125a, miR-23b, miR-210, miR-99b, and miR-101-2 were the top five identified miRNAs dysregulated in KIRP. The possible pathological roles of the candidate mRNAs and miRNAs identified in this study are presented in Fig. The molecular features presented in this study will offer new insights into the underlying mechanisms that are responsible for the initiation and progression of KIRC and KIRP. Moreover, the ability to diagnose KIRC and KIRP with a high certainty level will help pathologists accuratelydifferentiate the most common subtypes of RCC. In this way, appropriate clinical decision-making strategies can be obtained. Furthermore, the ability of artificial intelligence for accurate differentiation of RCC may reduce unnecessary intervention rates. Our attempts to develop molecular discriminating patterns had some limitations. First of all, we did not evaluate the molecular mechanism of the candidate RNAs in the RCC models. In vitro and in vivo studies are needed to be performed to achieve this goal. Second, we did not validate the identified RNAs in clinical samples. Future studies would address this issue in large-sample-sized studies. We believe that adding other features from mutations, polymorphisms, alterations in copy number, and DNA methylation platforms would effectively tackle the discriminating problem of RCC subtypes and improve their early detection.In this paper, deep learning-driven biomarkers were presented for discriminating common subtypes of RCC. Panels of 77 mRNAs and 73 miRNAs could discriminate the KIRC, KIRP, and KICH subtypes from each other with high accuracy. The CSN7A and miR-28 along with the NMD3 and miR-125a were the most frequent itemsets in the KIRC and KIRP association rules, respectively. Due to a frequent mutation in protein-coding regions and an elevated burden of unfolded proteins; an elevated protein turnover was necessary for those speedily dividing cancer cells. Hence, the inhibition of the UPS components appeared to be a hopeful strategy for KIRC therapy. The identified mRNAs and microRNAs in this study can regulate signal transduction, cell cycle machinery, and apoptosis and all are relevant contributors to carcinogenesis and cancer progression. Therefore, they may provide further insight into the pathogenesis, diagnosis, prognosis, and molecular-targeted therapy in RCC subtypes Fig. .Supplementary Information.