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Consensus criteria for traumatic encephalopathy syndrome (TES) specify that at least one core clinical feature of cognitive impairment (CI; e.g., difficulties with memory, executive function) or neurobehavioral dysregulation (ND; e.g., explosiveness, rage, and mood lability) be present and not fully accounted for by other health disorders. Associations between self-reported symptoms that mirror the core clinical features of TES?and how they may be related to concomitant medical conditions?remain unclear. The purpose of this study was to evaluate the association of medical conditions and football exposures with TES clinical features (CI+/?, ND+/?) in 1741 former professional American-style football (ASF) players (age, 57.7???13.9 years; professional seasons, 6.6???3.9 years). Demographics (age, race/ethnicity, current body mass index, age of first football exposure, use of performance-enhancing drugs, position played, and past concussion symptoms), self-reported medical conditions (anxiety, depression, attention-deficit hyperactivity disorder [ADHD], sleep apnea, headache, stroke, hypertension, heart disease, high cholesterol, erectile dysfunction, and low testosterone) were collected. Of 1741 participants, 7.4% were CI+ and/or ND+ (n?=?129). Participants who were CI+ or ND+ were more likely to report one or more coexisting medical conditions than participants who did not report CI or ND (odds ratio [OR]?=?2.04; 95% confidence interval: 1.25?3.47; p?=?0.003). Separate general linear models for each medical condition that adjusted for demographics and football-related factors identified significant associations between ADHD, diabetes, erectile dysfunction, headaches, sleep apnea, anxiety, and low testosterone and CI+ and/or ND+ (ORs?=?1.8?6.0). Chi-square automatic interaction detection (CHAID) multi-variable decision tree models that incorporated medical conditions and football exposures accurately differentiated former players meeting either CI or ND clinical criteria from those meeting none (accuracy?=?91.2?96.6%). CHAID identified combinations of depression, headache, sleep apnea, ADHD, and upper quartiles of concussion symptom history as most predictive of CI+ and/or ND+ status. CI+ and/or ND+ players were more likely to report medical conditions known to cause cognitive symptoms. Concussion exposure and medical conditions significantly increased the likelihood that a former ASF player would demonstrate cognitive or neurobehavioral dysfunction. Clinicians engaged with this population should consider whether treatable coexisting condition(s) could account for some portion of the clinical picture associated with TES presentation.

Motile Aeromonas Septicemia caused by Aeromonas hydrophila is an important bacterial disease of Nile tilapia (Oreochromis niloticus). This study aimed to evaluate the effects of infection on the blood parameters and on architectural integrity of tissues of the fish. Experimental A. hydrophila infection of O. niloticus juveniles was carried out using isolates from infected fish in the Kainji lake area. Following intraperitoneal injection, the effects of the bacteria on gross, hematological and histological integrity of the fish were analyzed. The bacterial isolate induced mortality and morbidity in the fish. There were significantly (p

Traumatic brain injury (TBI) has the highest incidence of all common neurological disorders, and poses a substantial public health burden. TBI is increasingly documented not only as an acute condition but also as a chronic disease with long-term consequences, including an increased risk of late-onset neurodegeneration. The first Lancet Neurology Commission on TBI, published in 2017, called for a concerted effort to tackle the global health problem posed by TBI. Since then, funding agencies have supported research both in high-income countries (HICs) and in low-income and middle-income countries (LMICs). In November 2020, the World Health Assembly, the decision-making body of WHO, passed resolution WHA73.10 for global actions on epilepsy and other neurological disorders, and WHO launched the Decade for Action on Road Safety plan in 2021. New knowledge has been generated by large observational studies, including those conducted under the umbrella of the International Traumatic Brain Injury Research (InTBIR) initiative, established as a collaboration of funding agencies in 2011. InTBIR has also provided a huge stimulus to collaborative research in TBI and has facilitated participation of global partners. The return on investment has been high, but many needs of patients with TBI remain unaddressed. This update to the 2017 Commission presents advances and discusses persisting and new challenges in prevention, clinical care, and research. In LMICs, the occurrence of TBI is driven by road traffic incidents, often involving vulnerable road users such as motorcyclists and pedestrians. In HICs, most TBI is caused by falls, particularly in older people (aged ≥65 years), who often have comorbidities. Risk factors such as frailty and alcohol misuse provide opportunities for targeted prevention actions. Little evidence exists to inform treatment of older patients, who have been commonly excluded from past clinical trials—consequently, appropriate evidence is urgently required. Although increasing age is associated with worse outcomes from TBI, age should not dictate limitations in therapy. However, patients injured by low-energy falls (who are mostly older people) are about 50% less likely to receive critical care or emergency interventions, compared with those injured by high-energy mechanisms, such as road traffic incidents. Mild TBI, defined as a Glasgow Coma sum score of 13–15, comprises most of the TBI cases (over 90%) presenting to hospital. Around 50% of adult patients with mild TBI presenting to hospital do not recover to pre-TBI levels of health by 6 months after their injury. Fewer than 10% of patients discharged after presenting to an emergency department for TBI in Europe currently receive follow-up. Structured follow-up after mild TBI should be considered good practice, and urgent research is needed to identify which patients with mild TBI are at risk for incomplete recovery. The selection of patients for CT is an important triage decision in mild TBI since it allows early identification of lesions that can trigger hospital admission or life-saving surgery. Current decision making for deciding on CT is inefficient, with 90–95% of scanned patients showing no intracranial injury but being subjected to radiation risks. InTBIR studies have shown that measurement of blood-based biomarkers adds value to previously proposed clinical decision rules, holding the potential to improve efficiency while reducing radiation exposure. Increased concentrations of biomarkers in the blood of patients with a normal presentation CT scan suggest structural brain damage, which is seen on MR scanning in up to 30% of patients with mild TBI. Advanced MRI, including diffusion tensor imaging and volumetric analyses, can identify additional injuries not detectable by visual inspection of standard clinical MR images. Thus, the absence of CT abnormalities does not exclude structural damage—an observation relevant to litigation procedures, to management of mild TBI, and when CT scans are insufficient to explain the severity of the clinical condition. Although blood-based protein biomarkers have been shown to have important roles in the evaluation of TBI, most available assays are for research use only. To date, there is only one vendor of such assays with regulatory clearance in Europe and the USA with an indication to rule out the need for CT imaging for patients with suspected TBI. Regulatory clearance is provided for a combination of biomarkers, although evidence is accumulating that a single biomarker can perform as well as a combination. Additional biomarkers and more clinical-use platforms are on the horizon, but cross-platform harmonisation of results is needed. Health-care efficiency would benefit from diversity in providers. In the intensive care setting, automated analysis of blood pressure and intracranial pressure with calculation of derived parameters can help individualise management of TBI. Interest in the identification of subgroups of patients who might benefit more from some specific therapeutic approaches than others represents a welcome shift towards precision medicine. Comparative-effectiveness research to identify best practice has delivered on expectations for providing evidence in support of best practices, both in adult and paediatric patients with TBI. Progress has also been made in improving outcome assessment after TBI. Key instruments have been translated into up to 20 languages and linguistically validated, and are now internationally available for clinical and research use. TBI affects multiple domains of functioning, and outcomes are affected by personal characteristics and life-course events, consistent with a multifactorial bio-psycho-socio-ecological model of TBI, as presented in the US National Academies of Sciences, Engineering, and Medicine (NASEM) 2022 report. Multidimensional assessment is desirable and might be best based on measurement of global functional impairment. More work is required to develop and implement recommendations for multidimensional assessment. Prediction of outcome is relevant to patients and their families, and can facilitate the benchmarking of quality of care. InTBIR studies have identified new building blocks (eg, blood biomarkers and quantitative CT analysis) to refine existing prognostic models. Further improvement in prognostication could come from MRI, genetics, and the integration of dynamic changes in patient status after presentation. Neurotrauma researchers traditionally seek translation of their research findings through publications, clinical guidelines, and industry collaborations. However, to effectively impact clinical care and outcome, interactions are also needed with research funders, regulators, and policy makers, and partnership with patient organisations. Such interactions are increasingly taking place, with exemplars including interactions with the All Party Parliamentary Group on Acquired Brain Injury in the UK, the production of the NASEM report in the USA, and interactions with the US Food and Drug Administration. More interactions should be encouraged, and future discussions with regulators should include debates around consent from patients with acute mental incapacity and data sharing. Data sharing is strongly advocated by funding agencies. From January 2023, the US National Institutes of Health will require upload of research data into public repositories, but the EU requires data controllers to safeguard data security and privacy regulation. The tension between open data-sharing and adherence to privacy regulation could be resolved by cross-dataset analyses on federated platforms, with the data remaining at their original safe location. Tools already exist for conventional statistical analyses on federated platforms, however federated machine learning requires further development. Support for further development of federated platforms, and neuroinformatics more generally, should be a priority. This update to the 2017 Commission presents new insights and challenges across a range of topics around TBI: epidemiology and prevention (section 1); system of care (section 2); clinical management (section 3); characterisation of TBI (section 4); outcome assessment (section 5); prognosis (Section 6); and new directions for acquiring and implementing evidence (section 7). Table 1 summarises key messages from this Commission and proposes recommendations for the way forward to advance research and clinical management of TBI.

BACKGROUND: The recovery of severe traumatic brain injury (TBI) survivors with long-term favorable outlook is understudied. Time to follow commands varies widely in this patient population but has important clinical implications. OBJECTIVE: To (1) evaluate time to follow commands in severe patients with TBI with favorable outcomes, (2) characterize their trajectory of recovery, and (3) identify predictors associated with delayed cognitive improvement. METHODS: Participants were recruited prospectively at a Level I trauma center through the Brain Trauma Research Center from 2003 to 2018. Inclusion criteria were age 16 to 80 years, Glasgow Coma Scale score ≤8 and motor score

Diffusion tensor imaging (DTI) literature on single-center studies contains conflicting results regarding acute effects of mild traumatic brain injury (mTBI) on white matter (WM) microstructure and the prognostic significance. This larger-scale multi-center DTI study aimed to determine how acute mTBI affects WM microstructure over time and how early WM changes affect long-term outcome. From Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI), a cohort study at 11 United States level 1 trauma centers, a total of 391 patients with acute mTBI ages 17 to 60 years were included and studied at two weeks and six months post-injury. Demographically matched friends or family of the participants were the control group (n = 148). Axial diffusivity (AD), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were the measures of WM microstructure. The primary outcome was the Glasgow Outcome Scale Extended (GOSE) score of injury-related functional limitations across broad life domains at six months post-injury. The AD, MD, and RD were higher and FA was lower in mTBI versus friend control (FC) at both two weeks and six months post-injury throughout most major WM tracts of the cerebral hemispheres. In the mTBI group, AD and, to a lesser extent, MD decreased in WM from two weeks to six months post-injury. At two weeks post-injury, global WM AD and MD were both independently associated with six-month incomplete recovery (GOSE

Study objectivesFast frequency sleep spindles are reduced in aging and Alzheimer's disease (AD), but the mechanisms and functional relevance of these deficits remain unclear. The study objective was to identify AD biomarkers associated with fast sleep spindle deficits in cognitively unimpaired older adults at risk for AD.MethodsFifty-eight cognitively unimpaired, β-amyloid-negative, older adults (mean ± SD; 61.4 ± 6.3 years, 38 female) enriched with parental history of AD (77.6%) and apolipoprotein E (APOE) ε4 positivity (25.9%) completed the study. Cerebrospinal fluid (CSF) biomarkers of central nervous system inflammation, β-amyloid and tau proteins, and neurodegeneration were combined with polysomnography (PSG) using high-density electroencephalography and assessment of overnight memory retention. Parallelized serial mediation models were used to assess indirect effects of age on fast frequency (13 to

BACKGROUND: The prognostic value of glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1) as day-of-injury predictors of functional outcome after traumatic brain injury is not well understood. GFAP is a protein found in glial cells and UCH-L1 is found in neurons, and these biomarkers have been cleared to aid in decision making regarding whether brain CT should be performed after traumatic brain injury. We aimed to quantify their prognostic accuracy and investigate whether these biomarkers contribute novel prognostic information to existing clinical models. METHODS: We enrolled patients from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) observational cohort study. TRACK-TBI includes patients 17 years and older who are evaluated for TBI at 18 US level 1 trauma centres. All patients receive head CT at evaluation, have adequate visual acuity and hearing preinjury, and are fluent in either English or Spanish. In our analysis, we included participants aged 17-90 years who had day-of-injury plasma samples for measurement of GFAP and UCH-L1 and completed 6-month assessments for outcome due to traumatic brain injury with the Glasgow Outcome Scale-Extended (GOSE-TBI). Biomarkers were analysed as continuous variables and in quintiles. This study is registered with ClinicalTrials.gov, NCT02119182. FINDINGS: We enrolled 2552 patients from Feb 26, 2014, to Aug 8, 2018. Of the 1696 participants with brain injury and data available at baseline and at 6 months who were included in the analysis, 120 (7·1%) died (GOSE-TBI=1), 235 (13·9%) had an unfavourable outcome (ie, GOSE-TBI ≤4), 1135 (66·9%) had incomplete recovery (ie, GOSE-TBI

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

This paper aims to investigate the corrosion behaviour of heat-treated standard duplex stainless steel (UNS S32205) in the recovery of bitumen from Nigerian-rich tar sand, using sodium hydroxide (NaOH) as the process solution. Appropriate material family needs to be identified for the exploitation of tar sand resources to ensure sustainable extraction of bitumen. UNS S32205 offers exceptional mechanical properties and excellent corrosion resistance which makes it a great material choice for demanding applications, including offshore platforms and construction projects. Samples of UNS 32205 were heat-treated and immersed in the digested tar sand, maintaining a process temperature of 90°C. The microstructure of the corrosion coupons was studied using SEM/EDS (Scanning Electron Microscope/Energy Dispersive Spectroscope), and the corrosion rate of the samples was derived using gravimetric technique following ASTM G31 standard. The SEM micrographs indicated varying degrees of pits associated with the passivity of UNS 32205 and an imbalance between its ferrite and austenite phases due to the presence of secondary precipitates formed during heat treatment.The normalized sample, having a corrosion rate of 0.053 mm/year, exhibited the best corrosion resistance, followed by oil quenching (0.054 mm/year), then annealing (0.076 mm/year), and water quenching (0.077 mm/year), where they all showed improved corrosion resistance over the as-received sample having a corrosion rate of 0.078 mm/year.

This dataset demonstrates the use of computational fragmentation-based and machine learning-aided drug discovery to generate new lead molecules for the treatment of hypertension. Specifically, the focus is on agents targeting the renin-angiotensin-aldosterone system (RAAS), commonly classified as Angiotensin-Converting Enzyme Inhibitors (ACEIs) and Angiotensin II Receptor Blockers (ARBs). The preliminary dataset was a target-specific, user-generated fragment library of 63 molecular fragments of the 26 approved ACEI and ARB molecules obtained from the ChEMBL and DrugBank molecular databases. This fragment library provided the primary input dataset to generate the new lead molecules presented in the dataset. The newly generated molecules were screened to check whether they met the criteria for oral drugs and comprised the ACEI or ARB core functional group criterion. Using unsupervised machine learning, the molecules that met the criterion were divided into clusters of drug classes based on their functional group allocation. This process led to three final output datasets, one containing the new ACEI molecules, another for the new ARB molecules, and the last for the new unassigned class molecules. This data can aid in the timely and efficient design of novel antihypertensive drugs. It can also be used in precision hypertension medicine for patients with treatment resistance, non-response or co-morbidities. Although this dataset is specific to antihypertensive agents, the model can be reused with minimal changes to produce new lead molecules for other health conditions.

This study examines the interaction impacts of carbon dioxide molecule emissions and population changes on financial development in Sub-Saharan Africa (SSA). The study used yearly time series data spanning the years 2000 to 2021. Following the PMG and FE results, the dynamic system GMM estimator was used in the study. The study found a significant inverse long-run relationship between carbon dioxide (CO2) emissions and financial development. Also, demographic changes have a significant positive impact on financial development. The interaction term findings demonstrate that changes in CO2 and GHG emissions have a negative and significant influence on the impact of the money supply ratio on financial development in SSA. The study suggests policies that support the adoption of financial aid or other incentives for initiatives that reduce CO2 emissions. Additionally, initiatives to support financial inclusion, uphold financial stability, encourage the expansion of infrastructure, advance social welfare, and ensure environmental sustainability should be made. Therefore, the SSA countries might benefit from their expanding populations to drive long-term economic expansion and improve living standards for their people.

This research assessed the rural cassava farmers’ agro-climatic information needs in Osun State, Nigeria. A multi-stage sampling procedure was used in selecting 210 respondents. Data were analysed in percentage and mean. Results revealed that radio/television ( = 1.56) and personal experience with nature ( = 1.41) were the most frequent sources of agro-climatic information. Many (65.2%) displayed high knowledge of cassava agro-climatic issues. Three-quarters (75.00%) of the respondents were favourably disposed to agro-climatic information. Furthermore, sources of best climate change adaptive cassava varieties planting materials for the region ( =4.34) and appropriate timing for cassava planting to beat the adverse effect of climate change ( =4.19) topped the list of needed agro-climatic information. There is a significant relationship between age (r= 0.972), years of formal schooling (r= 0.073), perception (r= 0.854), and their agro-climatic information needs. It was concluded that sources of best climate change adaptive cassava varieties planting materials and appropriate timing for cassava planting were prominent needed agro-climatic information. Climatic change adaptive and mitigating measures that represent most of the agro-climatic information needs of cassava farmers should be provided by governmental and non-governmental agencies through the most frequent sources of agro-climatic information.

Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict outcomes in comatose patients. Purpose To develop and evaluate a prognostic model combining deep learning of head CT scans and clinical information to predict long-term outcomes after sTBI. Materials and Methods This was a retrospective analysis of two prospectively collected databases. The model-building set included 537 patients (mean age, 40 years ± 17 [SD]; 422 men) from one institution from November 2002 to December 2018. Transfer learning and curriculum learning were applied to a convolutional neural network using admission head CT to predict mortality and unfavorable outcomes (Glasgow Outcomes Scale scores 1-3) at 6 months. This was combined with clinical input for a holistic fusion model. The models were evaluated using an independent internal test set and an external cohort of 220 patients with sTBI (mean age, 39 years ± 17; 166 men) from 18 institutions in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study from February 2014 to April 2018. The models were compared with the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model and the predictions of three neurosurgeons. Area under the receiver operating characteristic curve (AUC) was used as the main model performance metric. Results The fusion model had higher AUCs than did the IMPACT model in the prediction of mortality (AUC, 0.92 [95% CI: 0.86, 0.97] vs 0.80 [95% CI: 0.71, 0.88]; P < .001) and unfavorable outcomes (AUC, 0.88 [95% CI: 0.82, 0.94] vs 0.82 [95% CI: 0.75, 0.90]; P = .04) on the internal data set. For external TRACK-TBI testing, there was no evidence of a significant difference in the performance of any models compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.90) in the prediction of mortality. The Imaging model (AUC, 0.73; 95% CI: 0.66-0.81; P = .02) and the fusion model (AUC, 0.68; 95% CI: 0.60, 0.76; P = .02) underperformed as compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.89) in the prediction of unfavorable outcomes. The fusion model outperformed the predictions of the neurosurgeons. Conclusion A deep learning model of head CT and clinical information can be used to predict 6-month outcomes after severe traumatic brain injury. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Haller in this issue.

As the last opportunity to assess treatment effect modification in a controlled setting prior to formal approval, clinical trials are a critical tool for understanding the safety and efficacy of new treatments in diverse populations. Recruitment of diverse participants in Alzheimer's Disease (AD) clinical trials are therefore essential to increase the generalizability of study results, with diversity broadly described to be representative and inclusive. This representation of study participants is equally critical in longitudinal cohort (observational) studies, which will be key to understanding disease disparities and are often used to design adequately powered AD clinical trials. New and innovative recruitment initiatives and enhanced infrastructure facilitate increased participant diversity in AD clinical studies.

The Glasgow Outcome Scale-Extended (GOSE) is a functional outcome measure intended to place individuals with traumatic brain injury (TBI) into one of eight broad levels of injury-related disability. This simplicity is not always optimal, particularly when more granular assessment of individuals' injury recovery is desired. The GOSE, however, is customarily assessed using a multi-question interview that contains richer information than is reflected in the GOSE score. Using data from the multi-center Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study (N = 1544), we used item response theory (IRT) to evaluate whether rescoring the GOSE using IRT, which posits that a continuous latent variable (disability) underlies responses, can yield a more precise index of injury-related functional limitations. We fit IRT models to GOSE interview responses collected at three months post-injury. Each participant's level of functional limitation was estimated from the model (GOSE-IRT) and comparisons were made between IRT-based and standard (GOSE-Ordinal) scores. The IRT scoring resulted in 141 possible scores (vs. 7 GOSE-Ordinal scores in this sample of individuals with GOSE scores ranging between 2 and 8). Moreover, GOSE-IRT scores were significantly more strongly associated with measures of TBI-related symptoms, psychological symptoms, and quality of life. Our findings demonstrate that rescoring the GOSE interview using IRT yields more granular, meaningful measurement of injury-related functional limitations, while adding no additional respondent or examiner burden. This technique may have utility for many applications, such as clinical trials aiming to detect small treatment effects, and small-scale studies that need to maximize statistical efficiency.

Interest in AYA cancer care has increased globally over the recent past; however, most of this work disproportionately represents white, heterosexual, middle-income, educated, and able-bodied people. There is recognition in the literature that cancer care systems are not structured nor designed to adequately serve people of colour or other equity-denied groups, and the structural racism in the system prevents prevention, treatment, and delivery of care. This work seeks to examine structural racism and the ways that it permeates into the lived experiences of AYAs in their cancer care. This article represents the first phase of an 18-month, patient-oriented, Participatory Action Research project focused on cancer care for racialized AYAs that is situated within a broader program of research focused on transforming cancer care for AYAs. Semi-structured interviews were completed with 18 AYAs who self-identify as racialized, have lived experiences with cancer, and have received treatment in Canada. Following participant review of their transcripts, the transcripts were de-identified, and then coded by three separate authors. Five main themes were identified using thematic analysis, including the need to feel supported through experiences with (in)fertility, be heard and not dismissed, advocate for self and have others advocate for you, be in community, and resist compliance.

At least 11 per-and polyfluorinated alkyl substances (PFASs) were more prevalent during the dry season, whereas only PFBA, L-PFBS, L-PFOS, and PFOA were prevalent during the wet season in 11 WWTPs. The ∑21 PFAS levels in the influent and the effluent ranged from 137 to 3327 ng/L and 265–7,699 ng/L in the dry season and 61–2,953 ng/L and 171–3,458 ng/L in the wet season, respectively. The highest mean concentrations were observed in the influent and effluent for PFOA (586 ng/L) and L-PFBS (552 ng/L); and FOET (1,399 ng/L) and PFNA (811 ng/L) during dry and wet seasons, respectively. During the wet season, 6:2 FTS was observed at the highest concentrations, exhibiting 4,900 ng/L (66%) and 2,351 ng/L (39%), 1,950 ng/L (53%) in SST and BNR, respectively. Principal component analysis (PCA), hierarchical clustering (HCA), and PFHpA/PFOA, PFBA/PFOA, and PFNA/PFOA ratios revealed mixtures of PFAS sources into WWTPs. HIGHLIGHTS PFASs were detected in 11 South African wastewater treatment plants.; PFASs varied seasonally and within different process units.; Perfluoroalkyl sulfonic acids and fluorotelomers accounted for high concentrations of PFASs in WWTPs.; For the first time, the study reports on fluorotelomers in South African WWTPs and; Statistical methods revealed a mixture of PFAS sources – domestic discharge and atmospheric precipitation.;