Non-parametric Statistical Tests Research Articles

The quality of life in orthognathic surgery: surgery first versus traditional orthognathic surgery evaluation.

This study aims to evaluate the differences in terms of Quality of life and the degree of satisfaction with the result obtained between the two groups of patients treated with the orthodontic first approach and surgery first approach, through administered questionnaires inserted in the post-operative phase. A total of 40 patients who previously underwent orthognathic surgery were included in this study, 20 treated with the orthodontic first approach and 20 treated with the Surgery First Approach. The impact of orthognathic surgery on patients' quality of life was recorded through the administration of the OHIP-14 test, FACE-Q test scale and FACE-Q test. Comparison between the two groups was done using a nonparametric inferential statistical test, the Mann-Whitney U-test. The results showed no significant differences in terms of quality of life between the two groups. Patients treated with orthodontic first approach presented greater psychological distress and perceived their faces as unattractive. A worsening of the aesthetics of the face determined by the orthodontic approach before surgery, may not be decisive in the quality of life of patients, which will certainly be better after surgery. SFA and OFA determine in both cases a marked improvement in the patient's quality of life.

Comparing clinical measures of near point of convergence and accommodation from the lateral canthus and browline in a paediatric and adolescent population.

Clinicians measure the near point of convergence (NPC) and the amplitude of accommodation (AA) from the spectacle plane, the bridge of the nose or the lateral canthus when assessing visual function. These values are compared to standard clinical criteria to diagnose vergence and accommodation deficits, despite varying reference points. This prospective study explored measuring relative to the spectacle plane and from the lateral canthus for NPC and monocular AA, and the resulting clinical implications of diagnosing visual deficits. Participants were seen by a single clinician for an eye examination. NPC was measured from the forehead and the lateral canthus of the right eye. Monocular AA was measured from the brow and the lateral canthus. Differences between measurements were analysed using non-parametric statistical tests including Wilcoxon Signed Rank, as well as linear regression and a linear mixed effects model to adjust for inter-eye correlation and repeated measures. Chi-square tests were used to assess differences in rates of abnormal findings. Data were collected from 70 participants (53% female, median age 13 [11-15] years). On average, measuring NPC from the lateral canthus yielded a value 1.8 cm higher than measuring from the forehead. Measuring AA from the lateral canthus resulted in an average difference of 1.5 cm compared to measuring from the brow. A total of 39% and 76% of subjects failed NPC compared to clinical norms when measured from the forehead or the lateral canthus, respectively, while 7% and 40% failed AA when measured from the brow or the lateral canthus, respectively. With the variable anatomy of the eye, it is imperative to account for the measurement point when assessing visual function. Measuring from the lateral canthus greatly increased the failure rates for NPC and AA compared with measuring from the forehead and brow, respectively.

Performance of three commercially available large language models and one locally fine-tuned model at preparing formal letters to appeal medical insurance denials of radiotherapy services.

e13630 Background: As many as 60% of prior authorization requests are denied, yet coverage approval occurs for more than 60% of appeals for some therapies. Appeal processes encumber providers and increase burnout, but large language models (LLMs) may aid providers by drafting appeal letters. We evaluated LLM performance at this task for radiotherapy denials. Methods: Three commercially accessible LLMs were evaluated: generative pre-trained transformer 3.5 (GPT3.5), GPT4, and GPT4+web with internet search capacity (OpenAI, Inc., San Francisco, CA). A fourth LLM, GPT3.5-FT, was developed by fine-tuning GPT3.5 in a HIPAA-complaint local environment. The fine-tuning training data comprised 53 insurance denial appeal letters prepared by radiation oncologists and paired prompts describing the clinical history and appeal intent. Training data were enriched in appeal letters for proton radiotherapy, stereotactic body radiotherapy, and image-guided radiotherapy for myriad clinical scenarios. Twenty prompts, each requesting a letter for a simulated patient history, were programmatically presented to the LLMs. Three radiation oncologists, who were blinded to the LLM source, scored letter outputs across four domains: language syntax and semantics, clinical detail inclusion, clinical reasoning validity, and overall readiness for insurer submission. Additionally, one radiation oncologist scored the authenticity and relevance of literature sources cited in output letters, which were requested by several test prompts. Interobserver agreement between radiation oncologist scores was determined by Cohen’s kappa coefficient. Scores were compared between LLMs with non-parametric statistical tests. Results: Agreement between radiation oncologists’ scores was moderate-to-excellent across all domains (median κ = 0.68, minimum κ = 0.41). GPT3.5, GPT4, and GPT4+web drafted letters that, by mode average, were semantically and syntactically clear, included all provided clinical history without confabulation, clinically reasoned with few necessary revisions, and overall were submissible to an insurer with minor revisions. GPT4 and GPT4+web clinically reasoned better than GPT3.5 (p values < 0.001). In contrast, GPT3.5-FT performance was inferior to other LLMs across all domains (p values < 0.001). LLMs were poor at identifying, citing, and summarizing relevant literature unless provided in the prompt. Conclusions: LLMs can draft insurance appeal letters for radiotherapy services that require few revisions yet are poor at referencing relevant literature. Contrary to our hypothesis, fine-tuning with data from our department compromised LLM performance.

Prospective assessment of targeted symptom management on supportive care in cancer: An observational cohort study.

e24073 Background: In advanced cancer, patients may experience treatment-related symptoms or disease burden, necessitating multiple supportive care pharmacotherapies. However, managing these symptoms can lead to complex polypharmacy, potentially resulting in inefficacies or toxicities due to pharmacogenetic interactions. Our study focuses on targeting the most impactful symptom to streamline pharmacotherapy, potentially reducing the need for extensive polypharmacotherapy. By prioritizing management of this "target symptom”, our study seeks to optimize improve overall symptom relief and reduce the need for extensive polypharmacotherapy. Methods: Upon initial palliative care evaluation, the PERSONS score (Pain, Eating/cachexia, Rehabilitation/fatigue, Sleeping disorder, O2/dyspnea, Nausea/emesis, Suffering; each item rated 0-10, total score 0-70) was used to screen for cancer-related symptoms and identify a treatment-specific target symptom for each enrolled patient. Accordingly, a personalized palliative care intervention was implemented based on the identified target. Efficacy of this strategy was then assessed by monitoring changes in the PERSONS score after approximately 10 days of treatment. Non-parametric statistical tests were employed to evaluate differences in symptom distributions between baseline and post-treatment scores: the Wilcoxon signed-rank test for comparisons between two groups and the Kruskal-Wallis test for comparisons across more than two groups. All analyses were conducted using R v4.1. Results: Among 72 prospectively enrolled patients, most had intermediate KPS scores (30-40, 47%; 50-60, 33%) and were not on active treatment (73%); PaP score was A, B and C in 27%, 61% and 11% of patients, respectively. The median PERSONS score at baseline was 26 (range 9 to 51). Pain was identified as the target symptom in 33 (47%) of patients, followed by dyspnea (14%), fatigue (11%), suffering (11%), nausea/emesis (7%) and cachexia (7%). When identified as a target symptom, suffering had numerically higher scores (range, 7-10; Kruskal-Wallis p = .14). PERSONS scores across non-target symptoms were numerically higher in patients with fatigue, pain and nausea/emesis identified as target symptoms. After a median of 9.5 days (range 6 to 138) of targeted treatment, PERSONS scores across non-target symptoms were significantly lower (p = 7.9e-05; median difference = -6, range = -24 to +10), as was intensity of target symptoms (p = 7.2e-14). Conclusions: Focusing on a targeted symptom through a short treatment course not only reduced that specific symptom but also demonstrated clinically significant improvements in non-target symptoms. This comprehensive approach, emphasizing the interconnectedness of symptoms, has the potential to optimize patient care and enhance clinical outcomes in the context of advanced cancer management.

Spatio-temporal trends in long-term seasonal groundwater level of South-western Punjab using non-parametric statistical tests.

To manage groundwater resources and develop an action plan, it is crucial to understand the long-term behavior of groundwater level (GWL) fluctuations. In this study, Geographic Information System (GIS) and non-parametric statistical tests were applied for detecting long-term (1973 to 2020) spatio-temporal variations and trends in GWL from 137 observation wells evenly distributed across the south-western part of Punjab. This region has experienced significant changes in GWL over the decades. The non-parametric statistical tests included Mann-Kendall (MK), Sens's SlopeEstimator (SSE), and Innovative Trend Analysis (ITA). The study observed significant trends in GWL fluctuations before and after monsoon. The MK and SSE tests showed a statistically increasing trend in observation wells with about 65.7% and 67.2% increase before and after monsoon, respectively. The innovative trend analysis (ITA) also revealed a statistically increasing trend in observation wells with an increase of about 63.5% and 65.7% pre and post-monsoon season, respectively. The results indicate lowering of GWL in the northern districts of southwestern Punjab, while the southern districts experience rising GWLs. This discrepancy can be attributed to diverse agricultural activities and reduced over-exploitation of groundwater in the southern district due to soil salinity and the presence of brackish groundwater. These findings provide valuable insights into the dynamics of GWL in the studied region, highlighting notable trends associated with seasonal variations.

Accounting graduates’ perceptions on sustainability

Purpose This study aims to test whether the perceptions of graduates of accounting-related degrees on the implications of companies’ commitment to sustainable development strategies can be influenced by certain factors associated with their previous training and characteristics. Design/methodology/approach The study is based on measuring and analyzing the assessment carried out by a sample of students graduating in degrees that include accounting subjects for five consecutive years, from 2016–2017 to 2020–2021, both inclusive. Nonparametric statistical tests are used to determine the type of association between the factors that characterize the graduates and their degree of agreement with the training they received and its relationship with their role in implementing corporate social responsibility (CSR) and sustainability strategies in the company. Findings The study’s significant findings reveal that the respondents’ perception of the training they received and their opinion of certain benefits provided by sustainable development and CSR strategies in organizations are positively related. This insight is crucial, as it suggests that the training graduates receive plays a pivotal role in shaping their understanding and support for sustainability. The respondents’ opinions do not vary depending on their personal and/or work characteristics, except in the type of contract, as significant differences are observed between the self-employed and temporary or nonworking workers and between those with a permanent contract and those who are linked to the company with a temporary contract. Originality/value In a field where most studies focus on employee–employer relationships and human resource management policies, this research stands out. It delves deeper, not just into employees’ perception of sustainability but also into the causes of this perception. It explores what factors may be influencing employees’ opinions on sustainability, and importantly, it extends this analysis to graduates who will be in charge of these issues. This work covers a significant gap in the research, incorporating the study of variables such as personal characteristics and work-related aspects of employees and the training received in accounting matters.

Assessing Residential Building Energy Efficiency Using Evolutionary Dendritic Neural Regression

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain.

PENGARUH KOMPRES LIDAH BUAYA (ALOE VERA) TERHADAP PENURUNAN NYERI PEMBENGKAKAN PAYUDARA IBU MENYUSUI HARI 1-3 DI KLINIK CITRA MARINDA KECAMATAN DELITUA KAB. DELI SERDANG TAHUN 2023

Breast swelling is a breastfeeding trouble that mothers regularly revel in after giving start. Breast swelling can cause pain that influences the breastfeeding process. This look at aims to determine the impact of applying aloe vera poultices on lowering pain related to breast swelling in postpartum girls at Citra Marindal medical institution, District. Delitua Regency Deli Serdang, 2023. The sort of studies used is quantitative research with a quasi-experimental design with a pretest-posttest institution approach. The sample for this observe became all postpartum mothers with as much as sixteen stories the usage of the whole sampling method on the Citra Marindal clinic. The evaluation used is the Wilcoxon check. The outcomes of a non-parametric statistical test, the Wilcoxon check, confirmed that p-fee = <.half; which means applying aloe vera compresses reduces the pain due to breast swelling in postpartum girls. pointers for moms after childbirth to know about aloe vera compresses can lessen the pain because of swelling with advice from health professionals.

Clinical usability study of a home-based self-administration transcranial direct current stimulation for primary dysmenorrhea: A randomized controlled trial.

This study tested the usability of a home-based self-administration transcranial direct current stimulation (tDCS) device designed specifically for women's health needs. This is a single center triple blinded clinical usability study for a new wireless, Bluetooth-controlled wearable tDCS device for women's health. The study aims to evaluate the usability and effective blinding of a home-based tDCS system. A total of forty-nine women of reproductive age were randomly allocated (1:1) to receive one session of active tDCS (n = 24) or sham tDCS (n = 25) over the motor and dorsolateral prefrontal cortex. Each participant self-administered one 20-minute session without supervision following guidance on a software application alone. The System Usability Scale (SUS) and the Patient Global Impression of Change (PGIC) were used to evaluate the usability of the system. Regardless of sham or active conditions, all users found the system easy to use without the support of researchers. Usability scores were considered to be "excellent" in both groups and no significant difference was found between sham and active groups showing effective blinding of the device (Active group: 93.7 (83.1-97.5); Sham group 90 (86.2-95) p = 0.79) and PGIC (Active group: 2 (1-2.75); Sham group 2 (1-2) p = 0.99) using an unpaired t-test or non-parametric statistical tests accordingly. The new Bluetooth-controlled wearable tDCS device is easy, safe to use and completely controlled by a smartphone app. This device is focused on women's health and will be tested as an alternative treatment for chronic pelvic pain and mood disturbance associated with menstrual cycles in further research.

Ecological risk of metals in Andean water resources: A framework for early environmental assessment of mining projects in Peru

Metallic contaminants in Andean water resources influenced by mining activities poses risks to aquatic ecosystems and a challenge to regulatory agencies responsible for environmental compliance. In this study, the Ecological Risk Assessment (ERA) framework was adapted to assess dissolved heavy metal concentrations at 283 surface water monitoring stations near to six mining projects during the dry and wet seasons. Reports from OEFA-Peru on Early Environmental Assessment (EEA) were used to apply various criteria and non-parametric statistical tests. They included ecological, ecotoxicological, chemical, and regulatory factors. The main goal of this research was to identify, analyze, characterize, and compare the risks present at different trophic levels. These levels were categorized as T1 (Microalgae), T2 (Zooplankton and Benthic invertebrates), and T3 (Fish). Individual risk (IR) was estimated using the quotient model, while total risk (TR) was assessed using the additive probability rule. Rainbow trout (Oncorhynchus mykiss), representing trophic level T3, showed the highest sensitivity to Fe and Cu. Statistical tests ranked the IR as Fe > Cu > Zn > Mn > Pb (p < 0.01). The TR was more prevalent during the wet season compared to the dry season (p < 0.01). Notably, around 50 % of the monitoring stations (n = 142) were classified as high risk, and 9 % (n = 13) showed extremely high-risk values for Cu and Fe. The adapted ERA framework demonstrated great effectiveness in identifying critical points of metal contamination in high Andean aquatic ecosystems under mining influence. However, specialized studies are suggested that allow the sources of pollution to be associated with specific regulatory actions.

Riding for Focus as a Pathway to Improved Mental Health in Middle School Students

Youth mental health remains in a critical state even in a post-COVID pandemic world. Exercise is a well-regarded method to boost mental health and well-being in all age groups. The Riding 4 Focus (R4F) program is a cycling education program designed to equip students with basic cycling handling skills and introduce students to the lifetime physical activity of cycling. A secondary outcome of this program is to improve the behavioral health and well-being of adolescents in middle schools, primarily aged 10-14. The current study examined associations between participating in the R4F program and mental health and well-being in those with specific unmodifiable risk factors including gender, socioeconomic, and racial identity. Anonymous online surveys were collected from 3924 adolescent participants before the R4F program and 3289 students after the cycling program across 31 schools during the fall 2021 and spring 2022 semesters. Psychosocial well-being was quantified using the WHO-5 and PSC-17-Y metrics. Using non-parametric test statistics, the data shows a significant increase in the mental health and well-being of participants after the program. Further analysis revealed that participation in the R4F program was associated with improved psychological health and well-being of minority and low-income adolescents, as well as protective associations for both genders. Although these findings are encouraging, the effcacy of the program depends on a number of factors, and future studies are needed to assess causality. Outride, Loma Linda University School of Medicine, University of Redlands Summer Research Program, P20MD006988. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Spatio-Temporal Trend Analysis of Annual and Seasonal Rainfall in Ho Chi Minh City

Spatio-temporal variations and trends in climatic variables play an essential role in the adaptation and mitigation of climate change policy. This study analyzed spatial and temporal variability of annual and seasonal rainfall in Ho Chi Minh City based on non-parametric statistical trend tests and spatial interpolation techniques. In particular, Sen’s Slope Estimator and Inverse Distance Weighting (IDW) methods were applied to construct the spatial distribution of annual and seasonal rainfall over the city. The outcomes show that annual and seasonal rainfall experienced significant increasing trends. Generally, these findings draw out an overall picture of rainfall variability in terms of space and time, which makes it possible for local decision-makers to promote more promising action plans in order to respond to natural disasters in the context of climate change.

ADHD Patients with Suicidal Behaviour: Risk Factors, Comorbidities, and Clinical Profile: A Naturalistic Exploratory Study.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that disrupts brain functioning and is characterized by hyperactivity, impulsivity, and inattention. With varying presentations and complex etiological factors contributing to the development of ADHD, along with its persistence into adulthood, ADHD must be studied. Researchers have taken an interest in the relationship between ADHD and suicide, which is a serious public health concern with increasing prevalence rates in the Americas. The current literature reveals conflicting views on the importance of psychiatric comorbidities in the development of suicidal behaviours in ADHD patients. Therefore, this study aimed to determine whether there were significant differences between adult ADHD patients with suicide risk and adult ADHD patients without suicide risk. This study was a naturalistic retrospective chart review pilot study that used a sample of adults with a confirmed diagnosis of ADHD from January 2023 to August 2023. Using convenience sampling and sets of inclusion and exclusion criteria, patient data were sequentially collected from Med Access electronic medical records. The control and experimental groups each consisted of 50 patients (100) ranging from 19 to 58 years old. Our quantitative data were analyzed using non-parametric statistical tests, including the Chi-Square test and the Mann-Whitney U test. The results showed significant associations between ADHD patients with suicide risk and (1) borderline personality disorder; (2) binge eating disorder; (3) seven specific psychosocial risk factors; and (4) a higher number of antidepressant medication trials. No significant associations were found with other psychiatric disorders; however, there are important sex differences in terms of the risk factors. Our pilot study reveals several significant differences between adult ADHD patients with suicide risk and those without suicide risk. However, given our limited sample size and limitations, we hope our study encourages larger-scale studies to further investigate this relationship to improve its generalizability.

A variable neighborhood search for the median location problem with interconnected facilities

AbstractThe ‐median problem has been widely studied in the literature. However, there are several variants that include new constraints to the classical problem that make it more realistic. In this work, we study the variant that considers interconnected facilities, that is, the distances between each pair of facilities are less than or equal to a certain threshold . This optimization problem, usually known as the median location problem with interconnected facilities, consists of locating a set of interconnected facilities to minimize the distance between those interconnected facilities and the demand points. The large variety of real‐world applications that fit into this model makes it attractive to design an algorithm able to solve the problem efficiently. To this end, a procedure based on the variable neighborhood search methodology is designed and implemented by using problem‐dependent neighborhoods. Experimental results show that our proposal is able to reach most of the optimal solutions when they are known. Additionally, it outperforms previous state‐of‐the‐art methods in those instances where the optima are unknown. These results are further confirmed by conducting nonparametrical statistical tests.

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T

PurposeFurther acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge. MethodsThe effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin's Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV¯) and non-parametric statistical tests. ResultsThe simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV¯={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV¯={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI). ConclusionADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction.

Empowering Structural Integrity: YOLO-Based Crack Detection and ML-Driven Concrete Strength Prediction of Critical Infrastructure Caused due to Mining Operation.

In this paper, YOLO algorithm is used for detecting crack by incorporating machine learning approach utilized for predicting concrete strength and possible crack in critical infrastructure caused during mining operation. Over time, those fissures become more noticeable until they finally collapse. Big data is accumulating huge amount of data by utilizing remote sensing, earth penetrating RADAR, LiDAR, GPS, and satellite image data for large-scale operations. The performance of the proposed approach is compared with other state-of-the-art algorithms, such as, YOLO V3 and YOLO V8. To investigate the performance, four performances metric like mean absolute error (MAE), root mean square error (RMSE), relative standard error (RSE) and root relative squared error (RRSE) has considered. It is found that our approach is giving superior results in term of the compared performance metrics. Moreover, non-parametric statistical testing, such as, signed test, Wilcoxon Rank test and Friedman's test has been conducted to verify the dominance of proposed approach over others.

To Stage or Not to Stage? Direct-to-Implant Versus Staged Reconstruction for Implant-Based Breast Reconstruction

Abstract Goals/Purpose Implant-based breast reconstruction remains the common method of reconstruction after mastectomy in the US. An increasing number of centers are now offering direct-to-implant (DTI) breast reconstruction, avoiding staged reconstructions with tissue expander placement first. However, little is known regarding the need for revisions following DTI. In this study we sought to investigate short- and long-term complications and revisions following DTI versus staged reconstruction. Methods/Technique We retrospectively reviewed all patients who underwent bilateral nipple-sparing-mastectomies seeking implant-based breast reconstruction between September 2016 to September 2021. We extracted demographic and clinical information for the selected patients. Patients were divided in two cohorts: the DTI cohort, and the staged cohort. Baseline differences among the two groups were assessed by performing non-parametric statistical tests. Number of complications and revisions were assessed by employing chi-square. Logistic regression was then employed to adjust for possible confounders. Results/Complications During the study period, we identified 143 patients, 69 in the DTI cohort and 74 in the staged cohort. Patients in the staged cohort were more likely to have higher BMI (mean 26 kg/m2 versus 24.3 kg/m2 in the DTI cohort) and have undergone prior radiotherapy than those in the DTI cohort. No differences based on age, race/ethnicity or comorbidities were present. According to our analysis and within the follow up period time of average 21 months, the two cohorts had similar rates of minor and major short- and long-term surgical complications. After adjusting for potential confounding factors, the DTI group had a significantly higher rate of overall revisions (OR 2.73, 95% CI 1.23-6.07, p-value 0.01) and specifically revisions with implant exchange (0R 3.06, 95% CI 1.06-8.85, p-value 0.03). Most common reason for reason was asymmetry or contour deformity. However, the DTI cohort had significantly lower charges associated with their care during the follow up period (d=-100,178.14 US dollars, p-value=0.001). Complications rates were similar among the two groups. Conclusion An increasing number of patients are now interested in DTI breast reconstruction. According to the results of our study, DTI patients may have increased number of revisions but share similar rates of complications. DTI breast reconstruction is also associated with lower charges, even after accounting for the additional necessary revision procedures. Careful selection of patients for DTI and appropriate education on the need for revisions in the future is paramount.

Targeting transitioning lung monocytes/macrophages as treatment strategies in lung disease related to environmental exposures

BackgroundEnvironmental/occupational exposures cause significant lung diseases. Agricultural organic dust extracts (ODE) and bacterial component lipopolysaccharide (LPS) induce recruited, transitioning murine lung monocytes/macrophages, yet their cellular role remains unclear.MethodsCCR2 RFP+ mice were intratracheally instilled with high concentration ODE (25%), LPS (10 μg), or gram-positive peptidoglycan (PGN, 100 μg) for monocyte/macrophage cell-trafficking studies. CCR2 knockout (KO) mice and administration of intravenous clodronate liposomes strategies were employed to reduce circulating monocytes available for lung recruitment following LPS exposure. Lung tissues and bronchoalveolar lavage fluid (BALF) were collected. Pro-inflammatory and/or pro-fibrotic cytokines, chemokines, and lung extracellular matrix mediators were quantitated by ELISA. Infiltrating lung cells including monocyte/macrophage subpopulations, neutrophils, and lymphocytes were characterized by flow cytometry. Lung histopathology, collagen content, vimentin, and post-translational protein citrullination and malondialdehyde acetaldehyde (MAA) modification were quantitated. Parametric statistical tests (one-way ANOVA, Tukey’smultiple comparison) and nonparametric statistical (Kruskal–Wallis, Dunn’s multiple comparison) tests were used following Shapiro–Wilk testing for normality.ResultsIntratracheal instillation of ODE, LPS, or PGN robustly induced the recruitment of inflammatory CCR2+ CD11cintCD11bhi monocytes/macrophages and both CCR2+ and CCR2− CD11c−CD11bhi monocytes at 48 h. There were also increases in CCR2+ CD4+ and CD8+ T cells and NK cells. Despite reductions in LPS-induced lung infiltrating CD11cintCD11bhi cells (54% reduction), CCR2 knockout (KO) mice were not protected against LPS-induced inflammatory and pro-fibrotic consequences. Instead, compensatory increases in lung neutrophils and CCL2 and CCL7 release occurred. In contrast, the depletion of circulating monocytes through the administration of intravenous clodronate (vs. vehicle) liposomes 24 h prior to LPS exposure reduced LPS-induced infiltrating CD11cintCD11bhi monocyte-macrophage subpopulation by 59% without compensatory changes in other cell populations. Clodronate liposome pre-treatment significantly reduced LPS-induced IL-6 (66% reduction), matrix metalloproteinases (MMP)-3 (36%), MMP-8 (57%), tissue inhibitor of metalloproteinases (61%), fibronectin (38%), collagen content (22%), and vimentin (40%). LPS-induced lung protein citrullination and MAA modification, post-translational modifications implicated in lung disease, were reduced (39% and 48%) with clodronate vs. vehicle liposome.ConclusionHighly concentrated environmental/occupational exposures induced the recruitment of CCR2+ and CCR2− transitioning monocyte-macrophage and monocyte subpopulations and targeting peripheral monocytes may reduce the adverse lung consequences resulting from exposures to LPS-enriched inhalants.

The relationship between patient characteristics and analgesic self-medication behavior in pharmacies in Kesesi District, Pekalongan Regency

Self-medication is an effort to self-medicate without a doctor's prescription. Analgesics are medications used to reduce or relieve pain. The purpose of this study was to determine the relationship between patient characteristics and analgesic self-education behavior in pharmacies in Kesesi District, Pekalongan Regency. This study used an analytical observational method with a cross-sectional approach, the research instrument used a questionnaire involving 97 respondents in 3 pharmacies in Kesesi District, Pekalongan Regency. The sampling technique uses the consecutive sampling method. The independent variable is the characteristic and the dependent variable is the analgesic self-medication behavior. Data analysis using univariate and bivariate analysis with non-parametric rank spearman statistical test. The results showed that the age of 26-45 years was 50.5%, women were 66%, secondary education was 61.9%, self-employed by 47.7% and 32% of moderate-income people were more self-medicated analgesics. Some patients had good behavior in self-medication analgesics by 71.1%. The conclusion of this study was that patient characteristics including age (p = 0.014), education (p = 0.000), occupation (p = 0.000) and income (p = 0.000) had a relationship with analgesic self-medication behavior in pharmacies Kesesi District, Pekalongan Regency, while unrelated patient characteristics were gender (p = 0.237).

Spatiotemporal Variation and Causes of Typical Extreme Precipitation Events in Shandong Province over the Last 50 Years

In this study, based on hourly ERA5 reanalysis data from July to September, from 1971 to 2020, for Shandong Province, we used mathematical statistical analysis, the Mann–Kendall nonparametric statistical test, cluster analysis, and other methods to extract and analyze the spatiotemporal variation characteristics and causes of typical extreme precipitation events. The results indicated the following: (1) The total number and duration of precipitation events show a nonsignificant upward trend, while the average and extreme rainfall intensities show a nonsignificant downward trend. (2) Extreme precipitation events are primarily concentrated in Qingdao, Jinan, Heze, and Binzhou, with fewer events occurring in central Shandong Province. (3) Extreme precipitation events are classified into four types (namely, patterns I, II, III, and IV). Pattern I exhibits two rain peaks, with the primary rain peak occurring after the secondary rain peak. Similarly, pattern II also displays two rain peaks, with equivalent rainfall amounts for both peaks. In contrast, pattern III has multiple, evenly distributed rain peaks. Finally, pattern IV shows a rain peak during the first half of the precipitation event. Pattern I has the highest occurrence probability (46%), while pattern IV has the lowest (7%). (4) The spatial distributions of the different rain patterns are similar, with most being found in the eastern coastal and western regions. (5) Extreme precipitation events result from interactions between large-scale circulation configurations and mesoscale convective systems. The strong blocking situation and significant circulation transport at middle and low latitudes in East Asia, along with strong convergent uplift, abnormally high specific humidity, and high-water-vapor convergence centers, play crucial roles in supporting large-scale circulation systems and triggering mesoscale convective systems.