Category: Statistics

Aesthetic Plast Surg. 2022 Oct 27. doi: 10.1007/s00266-022-03151-8. Online ahead of print.

ABSTRACT

BACKGROUND: Dorsal preservation techniques have been preferred and gained popularity in recent years. The current study compares the effects of dorsal preservation and dorsal reduction rhinoplasty on nasal patency and aesthetic outcomes by using Patient-Reported Outcome Measures (PROMs) and rhinomanometry. To our knowledge, this is the first study to compare dorsal preservation and dorsal reduction techniques with rhinomanometry.

METHODS: This is a prospective study of 34 patients who underwent rhinoplasty between January 2021-June 2022. The patients were randomly selected preoperatively and divided into two groups as structural rhinoplasty (SR) and preservation rhinoplasty (PR). Nasal Obstruction and Symptom Evaluation (NOSE), Standardized Cosmesis and Health Nasal Outcomes Survey (SCHNOS) scales and rhinomanometric evaluation were performed preoperatively, at 3rd month and 12th month postoperatively.

RESULTS: Nineteen patients (10 female, 9 male) were in SR group, 15 patients (7 female, 8 male) were in PR group. There was not significant difference in terms of age and gender between groups. In both groups, NOSE, SCHNOS-O and SCHNOS-C results were found to be significantly lower at postoperative 3rd and 12th month compared to preoperatively (p < 0.001 for the entire SR group, p = 0.001 for the entire PR group). There was no significant difference between groups in terms of PROMs. Mean total nasal volume (TNV) at 12th month were statistically higher than preoperative value in PR group (p = 0.031). Also there was no significant difference in SR group and between groups in terms of rhinomanometry results.

CONCLUSION: Dorsal preservation with pushdown technique provides good functional and aesthetic results comparable with structural rhinoplasty.

LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 . A well-designed prospective clinical trial.

PMID:36302983 | DOI:10.1007/s00266-022-03151-8

Aesthetic Plast Surg. 2022 Oct 27. doi: 10.1007/s00266-022-03134-9. Online ahead of print.

ABSTRACT

OBJECTIVE: To observe the clinical effect between orbital septum incision and classical incision of double eyelid plasty.

METHODS: We retrospectively analyzed 381 patients who underwent double eyelid blepharoplasty in the Department of Plastic and Laser Cosmetology of Hunan Provincial People’s Hospital from January 2019 to December 2019. The patients were divided into two groups according to different surgical methods: group A (n = 146) received the classical method and group B (n = 235) received the orbital septum method. The incidence of early postoperative complications, scar depression from 6 months to 1 year after the operation, the condition of ‘meat strip’ (the accumulation of soft tissue in front of the tarsal plate after double eyelid surgery, including skin, muscle, and fascia fat, results in a hypertrophic appearance of the upper eyelid) below the double eyelid line, and the symmetry of double eyelids were analyzed and evaluated.

RESULTS: The total number of early postoperative complications in group A was seven cases (incidence rate: approximately 4.80%), and the total number of early postoperative complications in group B was two cases (incidence rate: approximately 0.85%), with a statistically significant difference (P < 0.05). The degree of scar depression in group B was significantly lighter than that in group A from 6 months to 1 year after the operation (P < 0.05). The score of ‘meat strip’ below the double eyelid line in group B was significantly lighter than that in group A (P < 0.05). The symmetry of double eyelids in group B was better than that in group A (P < 0.05) CONCLUSION: Compared to the classical double eyelid method, the orbital septum method has the advantages of reducing early postoperative complications, reducing the severity of the scar, slighting the ‘meat strip,’ and improving symmetry, which results in higher postoperative satisfaction LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors https://www.springer.com/00266 .

PMID:36302980 | DOI:10.1007/s00266-022-03134-9

Sci Rep. 2022 Oct 27;12(1):18121. doi: 10.1038/s41598-022-23102-2.

ABSTRACT

Statins are hypolipidaemic in human immunodeficiency virus (HIV) positive individuals. However, their effect on all-cause mortality and rate of discontinuation is unclear. We conducted a systematic review to evaluate the impact of statins on all-cause mortality, discontinuation rates, and risk of adverse effects among HIV patients on highly active antiretroviral therapy (HAART). We searched four electronic databases from inception until October 2021 for trials and cohort studies evaluating the effects of statin treatment versus placebo in HIV patients. Forty-seven studies involving 91,594 patients were included. Statins were associated with significantly lower risk of discontinuation (RR, 0.701; 95% CI 0.508-0.967; p = 0.031). The risk of all-cause mortality (RR, 0.994; 95% CI 0.561-1.588; p = 0.827), any adverse effects (RR, 0.780; 95% CI 0.564-1.077; p = 0.131) and, diabetes mellitus (RR, 0.272; 95% CI 0.031-2.393; p = 0.241) with statin treatment were lower but not statistically significant compared to placebo/control. Statin treatment was associated with a trend of higher but statistically insignificant risk of myalgia (RR, 1.341; 95% CI 0.770-2.333; p = 0.299), elevated creatine kinase (RR, 1.101; 95% CI 0.457-2.651; p = 0.830) and liver enzyme activities (RR, 1.709; 95% CI 0.605-4.831; p = 0.312). Clinicians should consider the nocebo effect in the effective management of PLWH on statins, who present with common adverse effects such as myalgia and, elevated levels of creatine kinase and liver enzymes.

PMID:36302940 | DOI:10.1038/s41598-022-23102-2

Sci Rep. 2022 Oct 27;12(1):18108. doi: 10.1038/s41598-022-22816-7.

ABSTRACT

While understanding the time evolution of Covid-19 pandemic is needed to plan economics and tune sanitary policies, a quantitative information of the recurrent epidemic waves is elusive. This work describes a statistical physics study of the subsequent waves in the epidemic spreading of Covid-19 and disclose the frequency components of the epidemic waves pattern over two years in United States, United Kingdom and Japan. These countries have been taken as representative cases of different containment policies such as “Mitigation” (USA and UK) and “Zero Covid” (Japan) policies. The supercritical phases in spreading have been identified by intervals with RIC-index > 0. We have used the wavelet transform of infection and fatality waves to get the spectral analysis showing a dominant component around 130 days. Data of the world dynamic clearly indicates also the crossover to a different phase due to the enforcement of vaccination campaign. In Japan and United Kingdom, we observed the emergence in the infection waves of a long period component (~ 170 days) during vaccination campaign. These results indicate slowing down of the epidemic spreading dynamics due to the vaccination campaign. Finally, we find an intrinsic difference between infection and fatality waves pointing to a non-trivial variation of the lethality due to different gene variants.

PMID:36302922 | DOI:10.1038/s41598-022-22816-7

Environ Monit Assess. 2022 Oct 28;195(1):44. doi: 10.1007/s10661-022-10561-3.

ABSTRACT

Farming has a plethora of difficult responsibilities, and plant monitoring is one of them. There is also an urgent need to increase the number of alternative techniques for detecting plant diseases, which is now lacking. The agriculture and agricultural support sectors in India provide employment for the great majority of the country’s people. In India, the agricultural production of the country is directly connected to the country’s economic growth rate. In order to sustain healthy plant development, a variety of processes must be followed, including consideration of environmental factors and water supply management for the optimal production of crops. It is inefficient and uncertain in its outcomes to use the traditional method of watering a lawn. The devastation of more than 18% of the world’s agricultural produce is caused by disease attacks on an annual basis. Because it is difficult to execute these activities manually, identifying plant diseases is essential to decreasing losses in the agricultural product business. In addition to diagnosing a wide range of plant ailments, our method also includes the identification of infections as a prophylactic step. Below is a detailed description of a farm-based module that includes numerous cloud data centers and data conversion devices for accurately monitoring and managing farm information and environmental elements. This procedure involves imaging the plant’s visually obvious signs in order to identify disease. It is recommended that the therapy be used in conjunction with an application to minimize any harm. Increased productivity as a result of the suggested approach would help both the agricultural and irrigation sectors. The plant area module is fitted with a mobile camera that captures images of all of the plants in the area, and all of the plants’ information is saved in a database, which is accessible from any computer with Internet access. It is planned to record information on the plant’s name, the type of illness that has been afflicted, and an image of the plant. In a wide range of applications, bots are used to collect images of various plants as well as to prevent disease transmission. To ensure that all information given is retained on the Internet, data is collected and stored in cloud storage as it becomes essential to regulate the condition. According to our findings from our research on wide images of healthy and ill fruit and plant leaves, real-time diagnosis of plant leaf diseases may be done with 98.78% accuracy in a laboratory environment. We utilized 40,000 photographs and then analyzed 10,000 photos to construct a DCDM deep learning model, which was then used to train additional models on the data set. Using a cloud-based image diagnostic and classification service, consumers may receive information about their condition in less than a second on average, with the process requiring only 0.349 s on average.

PMID:36302915 | DOI:10.1007/s10661-022-10561-3

Nat Commun. 2022 Oct 27;13(1):6400. doi: 10.1038/s41467-022-33666-2.

ABSTRACT

Shared cars will likely have larger annual vehicle driving distances than individually owned cars. This may accelerate passenger car retirement. Here we develop a semi-empirical lifetime-driving intensity model using statistics on Swedish vehicle retirement. This semi-empirical model is integrated with a carbon footprint model, which considers future decarbonization pathways. In this work, we show that the carbon footprint depends on the cumulative driving distance, which depends on both driving intensity and calendar aging. Higher driving intensities generally result in lower carbon footprints due to increased cumulative driving distance over the vehicle’s lifetime. Shared cars could decrease the carbon footprint by about 41% in 2050, if one shared vehicle replaces ten individually owned vehicles. However, potential empty travel by autonomous shared vehicles-the additional distance traveled to pick up passengers-may cause carbon footprints to increase. Hence, vehicle durability and empty travel should be considered when designing low-carbon car sharing systems.

PMID:36302850 | DOI:10.1038/s41467-022-33666-2

Sci Rep. 2022 Oct 27;12(1):18100. doi: 10.1038/s41598-022-22973-9.

ABSTRACT

The incidence of pancreatic ductal adenocarcinoma (PDAC) is different among males and females. This disparity cannot be fully explained by the difference in terms of exposure to known risk factors; therefore, the lower incidence in women could be attributed to sex-specific hormones. A two-phase association study was conducted in 12,387 female subjects (5436 PDAC cases and 6951 controls) to assess the effect on risk of developing PDAC of single nucleotide polymorphisms (SNPs) in 208 genes involved in oestrogen and pregnenolone biosynthesis and oestrogen-mediated signalling. In the discovery phase 14 polymorphisms showed a statistically significant association (P < 0.05). In the replication none of the findings were validated. In addition, a gene-based analysis was performed on the 208 selected genes. Four genes (NR5A2, MED1, NCOA2 and RUNX1) were associated with PDAC risk, but only NR5A2 showed an association (P = 4.08 × 10^-5) below the Bonferroni-corrected threshold of statistical significance. In conclusion, despite differences in incidence between males and females, our study did not identify an effect of common polymorphisms in the oestrogen and pregnenolone pathways in relation to PDAC susceptibility. However, we validated the previously reported association between NR5A2 gene variants and PDAC risk.

PMID:36302831 | DOI:10.1038/s41598-022-22973-9

Sci Rep. 2022 Oct 27;12(1):18094. doi: 10.1038/s41598-022-22819-4.

ABSTRACT

Despite the availability of efficacious direct-acting antiviral (DAA) therapy, the number of people infected with hepatitis C virus (HCV) continues to rise, and HCV remains a leading cause of liver-related morbidity, liver transplantation, and mortality. We developed and validated machine learning (ML) algorithms to predict DAA treatment failure. Using the HCV-TARGET registry of adults who initiated all-oral DAA treatment, we developed elastic net (EN), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) ML algorithms. Model performances were compared with multivariable logistic regression (MLR) by assessing C statistics and other prediction evaluation metrics. Among 6525 HCV-infected adults, 308 patients (4.7%) experienced DAA treatment failure. ML models performed similarly in predicting DAA treatment failure (C statistic [95% CI]: EN, 0.74 [0.69-0.79]; RF, 0.74 [0.69-0.80]; GBM, 0.72 [0.67-0.78]; FNN, 0.75 [0.70-0.80]), and all 4 outperformed MLR (C statistic [95% CI]: 0.51 [0.46-0.57]), and EN used the fewest predictors (n = 27). With Youden index, the EN had 58.4% sensitivity and 77.8% specificity, and nine patients were needed to evaluate to identify 1 DAA treatment failure. Over 60% treatment failure were classified in top three risk decile subgroups. EN-identified predictors included male sex, treatment < 8 weeks, treatment discontinuation due to adverse events, albumin level < 3.5 g/dL, total bilirubin level > 1.2 g/dL, advanced liver disease, and use of tobacco, alcohol, or vitamins. Addressing modifiable factors of DAA treatment failure may reduce the burden of retreatment. Machine learning algorithms have the potential to inform public health policies regarding curative treatment of HCV.

PMID:36302828 | DOI:10.1038/s41598-022-22819-4

Sci Rep. 2022 Oct 27;12(1):18061. doi: 10.1038/s41598-022-22155-7.

ABSTRACT

Stray non-breeding cats (stray) represent the largest heterogeneous cat population subject to natural selection, while populations of the Siamese (SIAM) and Oriental Shorthair (OSH) breeds developed through intensive artificial selection for aesthetic traits. Runs of homozygosity (ROH) and demographic measures are useful tools to discover chromosomal regions of recent selection and to characterize genetic diversity in domestic cat populations. To achieve this, we genotyped 150 stray and 26 household non-breeding cats (household) on the Illumina feline 63 K SNP BeadChip and compared them to SIAM and OSH. The 50% decay value of squared correlation coefficients (r²) in stray (0.23), household (0.25), OSH (0.24) and SIAM (0.25) corresponded to a mean marker distance of 1.12 Kb, 4.55 Kb, 62.50 Kb and 175.07 Kb, respectively. The effective population size (N_e) decreased in the current generation to 55 in stray, 11 in household, 9 in OSH and 7 in SIAM. In the recent generation, the increase in inbreeding per generation (ΔF) reached its maximum values of 0.0090, 0.0443, 0.0561 and 0.0710 in stray, household, OSH and SIAM, respectively. The genomic inbreeding coefficient (F_ROH) based on ROH was calculated for three length categories. The F_ROH was between 0.014 (F_ROH60) and 0.020 (F_ROH5) for stray, between 0.018 (F_ROH60) and 0.024 (F_ROH5) for household, between 0.048 (F_ROH60) and 0.069 (F_ROH5) for OSH and between 0.053 (F_ROH60) and 0.073 (F_ROH5) for SIAM. We identified nine unique selective regions for stray through genome-wide analyses for regions with reduced heterozygosity based on F_ST statistics. Genes in these regions have previously been associated with reproduction (BUB1B), motor/neurological behavior (GPHN, GABRB3), cold-induced thermogenesis (DIO2, TSHR), immune system development (TSHR), viral carcinogenesis (GTF2A1), host immune response against bacteria, viruses, chemoattractant and cancer cells (PLCB2, BAHD1, TIGAR), and lifespan and aging (BUB1B, FGF23). In addition, we identified twelve unique selective regions for OSH containing candidate genes for a wide range of coat colors and patterns (ADAMTS20, KITLG, TYR, TYRO3-a MITF regulator, GPNMB, FGF7, RAB38) as well as congenital heart defects (PDE4D, PKP2) and gastrointestinal disorders (NLGN1, ALDH1B1). Genes in stray that represent unique selective events indicate, at least in part, natural selection for environmental adaptation and resistance to infectious disease, and should be the subject of future research. Stray cats represent an important genetic resource and have the potential to become a research model for disease resistance and longevity, which is why we recommend preserving semen before neutering.

PMID:36302822 | DOI:10.1038/s41598-022-22155-7

Sci Rep. 2022 Oct 27;12(1):18042. doi: 10.1038/s41598-022-22130-2.

ABSTRACT

Modern money transfer services are convenient, attracting fraudulent actors to run scams in which victims are deceived into transferring funds to fraudulent accounts. Machine learning models are broadly applied due to the poor fraud detection performance of traditional rule-based approaches. Learning directly from raw transaction data is impractical due to its high-dimensional nature; most studies construct features instead by extracting patterns from raw transaction data. Past literature categorizes these features into recency, frequency, monetary, and anomaly detection features. We use various machine learning algorithms to examine the performance of features in these four categories with real transaction data; we compare them with the performance of our feature generation guideline based on the statistical perspectives and characteristics of (non)-fraudulent accounts. The results show that except for the monetary category, other feature categories used in the literature perform poorly regardless of which machine learning algorithm is used; anomaly detection features perform the worst. We find that even statistical features generated based on financial knowledge yield limited performance on a real transaction dataset. Our atypical detection characteristic of normal accounts improves the ability to distinguish them from fraudulent accounts and hence improves the overall detection results, outperforming other existent methods.

PMID:36302818 | DOI:10.1038/s41598-022-22130-2