Firmicutes (46.24%), Bacteroidetes (34.19%), Proteobacteria (15.11%), with the particular presence of Spirochaetes, verrucomicrobia and Synergistetes, while those from artisanal production were colonized by Firmicutes (92.06%), Proteobacteria (4.21%) and Actinobacteria (2.09%). Furthermore, 43 AVSs of archaea were detected only in POME from industrial plants and assigned to Crenarchaeota, Diapherotrites, Euryarchaeota and Nanoarchaeaeota phyla, populated mainly by many methane-forming archaea. Definitively, the microbial community composition of POME from both type of processing was markedly different, showing that the history of these ecosystems and various processing conditions have a great impact on each microbial community structure and diversity. By improving knowledge about this microbiome, the results also provide insight into the potential microbial contaminants of soils and rivers receiving these wastewaters.Palm Oil Mill Effluents (POME) are complex fermentative substrates which habour diverse native microbial contaminants. However, knowledge on the microbiota community shift caused by the anthropogenic effects of POME in the environment is up to date still to be extensively documented. In this study, the bacterial and archaeal communities of POME from two palm oil processing systems  were investigated by Illumina MiSeq Platform. Despite the common characteristics of these wastewaters, we found that their microbial communities were significantly different with regard to their diversity and relative abundance of their different Amplicon Sequence Variants (ASV). Indeed, POME from industrial plants harboured as dominant phyla  Elaeis Guineensis) is the most widely used vegetable oil in the world. Grown only in the tropics, the plant is very productive and offers a far greater yield at a lower cost of production than other vegetable oils, thus standing up from them1. The oil from the palm tree is of high-quality and primarily used in developing countries for cooking, but also in food products, detergents, cosmetics and, to a small extent biofuel2. Plantations are spreading across Asia, Latin America and Africa; with Côte d'Ivoire, ranking second producers of palm oil in Africa, with an annual production of about 400,000 tons3 (CNUCED 2016). According to previous reports4, unlike in South-East Asia, where the processing of crude palm oil is entirely undertaken by agro-industries in high-technology well equipped mills, palm oil processing in most of African countries is undertaken by three distinct groups of actors: (i) traditional producers using basically manual methods with the use of rudimentary tools, (ii) small-scale  producers using various low-efficiency machineries; and (iii) finally the industrial mills with technologically up-to-date machinery, established by agro-industrial complexes. It is also a recurring situation in Cote d’Ivoire, where the number of traditional producers is increasing, due to the strong demand for oilseed products and recent technological and scientific advances in this sector.Palm oil, squeezed from the fruits of the oil palm tree  end up as POME6. Wastewaters from such intensive agricultural activity typically have high concentrations of organic matters and nutrients. POMEs are viscous, brownish liquid containing about 95–96% water, 0.6–0.7% oil and 4–5% total solids, acidic (pH 4–5) with high organic content 7; resulting in high pollutant loads. According to the standards on industrial effluents discharge in Côte d’Ivoire, the limit values are 300 mg/L for COD, 100 mg/L for BOD5 and the pH should be in the range of 6.5–8.5. Therefore, these wastewaters generate particular problems and challenges to producers, since high organic matter contents can lead to effluent management issues if they are allowed to discharge directly into receiving environments. For this reason, POME must be treated prior to disposal. However, when it comes to liquid waste management, most traditional and small-scale palm oil processors do not adhere to any environmental protection practices, while the environmental awareness level of the operators in this industrial area is low as reported in previous studies8. In addition, traditional processors operate so close to nature that they simply return liquids to the surrounding bushes. The discharged quantities are so small that the ground easily absorbs the waste matter and the operators have not yet seen their activities as injurious to their surroundings8, whereas in short and medium terms, the reverse effects on environment, in particular, on soils and waterways in which these effluents are discharged become visible.Palm oil worldwide production and demand is rapidly and intensively increasing; in consequence, its production results in the generation of large quantities of polluted wastewater commonly referred to as palm oil mill effluents (POME)9, while some could appear as potential bioindicators in the receiving waterways after final discharging10. In addition, if the physicochemical properties of POME are well documented, this is not the case for its microbiological aspects, which until then seem to have been little explored. This reveals a dearth of information on this microbiome and justifies the need for a thorough understanding of it. Consequently, the mastery of this microbiome lays a basis to promote a better understanding of the nature and types of microorganisms populating POME. Therefore, to ensure a sustainable practice of palm oil processing, it is essential to have more knowledge on the bacterial population, diversity and how they are related to the biodegradation process and/or severity of pollution of discharging water or soil. In this study, Illumina MiSeq sequencing was used to investigate the bacterial community composition of POME from two types of oil production  in four geographical localities of Côte d’Ivoire. We compared the microbial community structures of POME samples with the aim to determine the abundance and diversity of bacterial communities and provide insight of the potential functional microorganisms able to achieve higher reduction of organic load present in POME and their potential level of discharging environment microbial pollution.Moreover, environmental and social concerns are amplified by the escalating expansion of oil palm plantations and uncontrolled processing in tropical areas. Undoubtedly, environmental contamination is a relevant problem which has a negative impact on human health until today. Many approaches have been used to monitor and reduce this problem but, it still remains a difficult issue. To ensure both humans and environment security against adverse effects of this pollution, a deep insight into the pollutant load, particularly the microbial communities of these effluents must be designed. Indeed, it has long been recognized that certain microbial groups in waste are responsible for breaking down various organic compounds and for the suppression of pathogens in wasteThe physicochemical characteristics of POME samples collected in four locations of palm oil production in Côte d’Ivoire are presented in Table A total of 661,585 raw 16S V4–V5 sequences were obtained from twelve 12) out of 15 POME samples collected from four (4) localities of Côte d’Ivoire. The numbers of 16S V4–V5 sequences ranged from 32,448 to 74,934 among the samples, with samples from industrial oil production (e.g. AB3) having the highest number of bacteria ASV (439) and those of artisanal production (e.g. ZO3) the smallest number of ASV (30)  or Bacteroidetes at more than 80% (SI1) , with an average relative abundance of 90.28%; except in ZO samples where dominance is ensured by Ruminococaceae (62.01%) and Clostridiaceae 1 (22.08%). This family was followed by other families from various phyla notably Acetobacteraceae (21.73%) and Prevotellaceae (8.10%) in AB samples, Bifidobacteriaceae (4.08%) in DA samples and Prevotellaceae (38.88%) in SI samples. Families classified as “others” with relative abundance ˂ 0.1% and the non-attributed families represented 1.45% and 0.63% respectively , Prevotella (15.01%), Acetobacter (10.39%), Ruminiclostridium (7.45%), Clostridium sensus stricto (5.07%) and Sporolactobacillus (1.94%) were on average, the relatively abundant genera in POME samples. The others with a relative abundance ˂ 0.1% represented 13.12% of the total ASV  and Aboisso (AB) which were populated by Lactobacillus, Acetobacter, Thermoanaerobacterium and Prevotella , Methanomethylophilaceae (1.3%), Methanobacteriaceae (1.1%), Methanofastidiosaceae (0.35%), Methanoregulaceae (0.25%) and Methanospirillaceae (0.14%) on average  were used to measure the species richness and diversity within the bacterial community of POME, where higher values of indices can be regarded as higher genetic diversity at a production area or type. In general, POME from industrial oil processing showed the highest number of ASV and alpha diversity indices, particularly some samples  from Aboisso in the South, which displays a higher bacterial diversity compared to the others. In this type of production, effluents mentioned above are all put to economically useful purpose through a biotreatment process. Thus, the intrinsic variation in microbial parameters within this production site would be based on nutrient availability and microbial enrichment over time, from the factory outlet, its stay through the lagoon station until its biodegradation in the digestion tank. This is totally different from the practices in the other types of production where effluents, once produced have just enough time to cool before being poured into nature, without allowing prior microbial enrichment. The observations made in this study were supported by other studies20 who similarly stated that the system performance and the microbial community of the activated sludge were different based on the operating conditions. In the same way, some authors suggested that bacteria playing important roles in the biodegradation at the early phases might become excessively dominant, hence modifying the diversity indices10. In addition, the high organic matter concentration, readily available to most bacteria could contribute to the increment of the bacterial population21, hence reducing the Shannon and Eveness indices in the early phases of POME treatment.POME samples of the present study were characterized by a low pH and high phenolics, total solids (TS) and fatty matters contents, which were higher than the discharge effluent standards in Cote d’Ivoire22. This suggests that POME from Côte d’Ivoire would contain a high microbial diversity with a large reservoir of genetic variability. Besides, a significant difference of the bacterial community structure was observed according to the production areas and the type of palm oil production. Multivariate analyses through PCoA using Bray–Curtis dissimilarity and clustered heat map performed with the top 20 bacterial OTUs indicated that microbial community structures of samples from the same type of palm oil processing and the same locality were closer, except those from Aboisso located in the South-East of Côte d’Ivoire. Such discrepancy, particularly in samples from Aboisso seems obvious, due to the discrepancy in the sample collection points. Indeed, as previously reported23, the microbial structural differences between samples of various regions can be attributed to geographic location associated to other environmental factors such as elevation, climate, temperature, oxygen level, atmospheric pressure, sunlight, and length of sunlight radiation which may play roles in modulating the microbiome. However, this justification is not valid in the case of this study, at least for the first two factors mentioned. Indeed, statistical analyses by Mantel test showed that the species Bray–Curtis dissimilarity matrix did not have a significant relationship with environmental parameters of POME samples in one hand  and with the geographical separation of samples  in the other hand. Thus, POME from each production type or area might have been subjected to different kinds of external factors and environment stress along the final discharge exposure which potentially promoting microbial changes and affecting microbial community shift. In fact, in artisanal production areas, in particular ZO and DA, there are no post-production management methods for effluents before their final disposal. So once produced, these effluents just have a short time (maximum 48 h) to cool in tanks , as their presence was associated to the acidic characteristic of POME the by production of lactic acid22. Some authors linked their origin to the remaining debris of palm fruit mesocarp and the oil palm empty fruit bunch generated during the crude palm oil extraction process30. In addition, these microorganisms have robust stabilities with respect to the oxygen and their capacity to generate a series of antimicrobial compounds that do compromise the viability of other bacteria, thus making them the dominant groups of these effluents. Moreover species of Lactobacillus genus are ubiquitous in the environment, and so have been found even in sewages, which implies an extraordinary adaptability with respect to negative physical–chemical factors, similar to those of POME and a response capacity that has not been assessed sufficiently31. In addition, Lactobacillus genus is a heterogeneous group of lactic acid bacteria (LAB) with important implications in wastewater treatment and in food and feed biotechnology. Moreover, the particular dominance of Ruminiclostridium genus in POME from the western Côte d’Ivoire (Zouan hounien) could be related to the specific richness of these effluents in total solids, as members belonging to Ruminococcaceae family includes species able to produce a wide variety of carbohydrate-active enzymes for enhancing cellulosic biomass degradation. And this is useful for the conversion of lignocellulosic biomass of POME to valuable products, such as transportation biofuels32.Industrial production POMEs bacteria harboured Bacteroidetes and Proteobacteria were mainly represented in industrial production POME, respectively by Prevotellaceae and Acetobacteraceae families. Prevotellaceae family members were described as saccharolytic fermentative anaerobes involved in acidogenesis and promoted acetic acid production during anaerobic POME treatment33, explaining their massive presence in samples collected in the bioreactor tank in Aboisso (AB). Many of them were related to the organisms involved in the pollutant degradation, which suggest their importance for wastewater treatment22. Proteobacteria play a central role in the biological processes involved in the removal of organic matter and nitrogen in the ecoditch systems, but the distributions of subdivision may differ depending on the ambient environment, its salinity, and whether they are facing aerobic or anaerobic conditions34. Some members of Prevotellaceae family are lipase producer35, an important feature for oil and grease degradation in wastewaters and thus could fully contribute to reduce the fat which is abundant in POME from traditional oil production. Acetic acid bacteria (AAB) from Proteobacteria phylum are widely distributed in acidic environments36, thus justifying their presence in POME, an acidic microbiome, notably in AB samples which were more acidic. Members of Acetobacteraceae family are characterized by their potentiality to oxidize alcohols and sugars into acetic acid and strains of the genus Acetobacter, the most representative taxon of this phylum are referred to as decaying bacteria, responsible for the degradation of different substrates37.The other dominant phyla 38, but also in the digestive system of humans and animals. They consist of halophiles, thermacidophiles and methanogens and play quite important role in converting or removing carbon, nitrogen, phosphorus, and other pollutants into environmentally friendly materials39. Four archaeal phyla, namely Crenarchaeota, Diapherotrites, Euryarchaeota, Nanoarchaeaeota were found in this study. Nanoarchaeaeota and Euryarchaeota were the only phyla detected at the factory outlet for both industrial production locations (AB and SI), highlighting a potential geographical influence instead of the similar chemical characteristics of POME. Previous studies reported the phylum Euryarchaeota as predominant archaea in municipal anaerobic sludge digesters40. Throughout the entire treatment process in AB, POME was enriched in supplementary phyla, notably in Euryarchaeota at the lagoon station output (AB2) and in Euryarchaeota, Crenarchaeota and Diapherotrites at the bioreactor step , probably due to animal farming surrounding the lagoon station and the use of cow dung as inoculum in the biorector. This was consistent with previous studies in which archaea, notably Methanosarcina species were reported to dominate in a process treating cattle manure41. Most of the family taxa from these phyla identified in POME appeared to be methanogen archaea. Indeed, it has been reported that, methanogens or methane-forming archaea are the most important in wastewater treatment plants42.The distributions of archaeal abundance in POME were poorly understood, which increased difficulty in the interpretation of this study. Indeed, they were exclusively detected in POME from industrial oil processing. Archaea could exist in extreme environments, such as the hot spring and acidic environmentFirmicutes, Bacteroidetes and Proteobacteria and archaea communities dominated by methane-forming species; while POMEs from artisanal production were colonized by Firmicutes and Proteobacteria. Definitively, the microbial community composition of POME ecosystems in both industrial and artisanal plants was markedly different, showing that the history of these ecosystems and several potential factors including post production practices and micro environmental conditions have a great impact on their community structure and diversity. Thus, this work represents a step forward for providing valuable insight into the microbiology of these polluting wastewaters. Further research allowing to understand the enzymatic machinery and the bioremediation capacities of these microorganisms may offers a good opportunity to solve the environment problem linked to palm oil mill wastewater in Cote d’Ivoire.The present study reported on the bacterial and archaeal communities of POME obtained from two types of palm oil processing in four geographical localities. The specific details of bacteria and archaea populating these wastewaters were depicted. We found that industrial production POMEs harboured as dominant bacteria phyla In Côte d’Ivoire, different areas, going from South-East to the West are famous for the cultivation and the processing of palm seeds into palm oil. Two types of processing  are implemented either by companies or individuals, treating before discharging or directly discharging their effluents into receiving stream, ground water, and soil. Thus, samples were collected taking into account both types of processing in four localities notably Zouan Houien  and Dabore (5° 49′ 59.999″ N 5° 22′ 0.001″ W) for artisanal processing in the western, and Sikensi (5° 40′ 34.021″ N 4° 34′ 32.902″ W) and Aboisso Ayenouan  for industrial processing in the southern. Permission for POME sampling was obtained from the Centre Ivoirien Anti Pollution (CIAPOL), the relevant national institution dealing with pollution issues.For the production areas with an effluent treatment station, in particular for Aboisso (AB), different collection points have been identified for samples collection Fig. . These a43. The complete reagent mixture (25 μl) contained 30 ng of sample DNA, 1X Phusion HF Buffer, 0.2 mM dNTPs mix, 0.2 μM each primer, 3% DMSO, 0.5 U Phusion Hot Start II DNA pol. and molecular Grade H2O. The PCR amplification conditions were as follows: an initial denaturation at 94 °C for 3 min followed by 35 cycles of 94 °C for 45 s, 50 °C for 60 s, and 72 °C for 90 s, and a final extension at 72 °C for 10 min. Resulting amplicons were cleaned, pooled, quantified and the sequencing was performed with Illumina MiSeq PE 300 following the procedures of genomics platform of CERMO FC .Three POME samples from each collection point were extracted and pooled to obtain a complete microbial community information. Then metagenomic DNAs were extracted from 2 ml of decanted POME sample mix using E.Z.N.A. soil DNA kit  following the manufacturer’s instructions. DNA was stored at − 20 °C until further processing. For accessing POME microbiota, the V4–V5 hypervariable region of 16S rRNA gene was amplified using primers 515F (5′-GTGYCAGCMGCCGCGGTAA-3) and 926R (5-CCGYCAATTYMTTTRAGTTT-3)44. Data files obtained were imported and processed in the R-environment45 using various codes and inbuilt functions available in different R-packages. These files were filtered and trimmed using the filterAndTrim function of the dada2 package46 and bases with low-quality scores were discarded. DADA 2 (Divisive Amplicon Denoising Algorithm) pipeline was used for processed the filtered data files. The steps of dereplication, core denoising algorithm and merging of the base pairs were carried out to obtain the full denoised sequences. Then, the removeBimeraDenovo function of the dada2 package was used for removing chimeras. ASV sequences were assigned to a taxonomy using the Silva (Silva_nr_v132) database47 with assignTaxonomy function of the same dada2 package and a phyloseq data object was created. A phylogenetic tree for the taxa was constructed using the R-package ape48 using the prune_taxa functions of the phyloseq package in R49.Raw sequences from all samples analysed were demultiplexed and adapters and barcodes were removed. Then, the sequences quality was controlled using FastQC v0.11.5 according to a previously established procedure50 after rarefication. The rarefaction depth was 12,321 reads per sample. Plot for relative taxa abundances was made using the microbiome package51 using the entire 16S dataset. Chao1, Pielou, Simpson and Shannon index estimates of alpha diversity were measured from each sample using the phyloseq package. Kruskal–Wallis test was performed among sample categories while measuring Shannon and Chao1 index. Principal Coordinate Analysis (PCoA) was performed using Bray–Curtis dissimilarity matrix52 between samples and visualized using the phyloseq package49. Stratified permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was conducted on all PCoA with the vegan package to observe the statistical significance of clusters according to the sample categories. To assess the statistical significance (p < 0.05) p-values for multiple comparisons were adjusted according to the Benjamini and Hochberg method to control False Discovery Rate53 while performing multiple testing on taxa abundance according to sample categories. For heatmap analysis, data was log-transformed (log xi + 1) and the plot was done from top 20 ASV using microbiome R package51. All data were analysed in R version 3.6.3. And finally, mantel test was performed to assess correlations between species abundance and environmental parameter on the one hand and between species abundance and geographic distance on the other hand, using vegan and geosphere packages in R.The phyloseq object was used to calculate microbial abundances, α, β diversity analysis and to carried out statistical tests from various R packages


Patients with sepsis and immobility in the intensive care unit are associated with muscle weakness, and early mobilisation can counteract it. However, during septic shock, mobilisation is often delayed due to the severity of the illness. Neuromuscular electrical stimulation (NMES) may be an alternative to mobilise these patients early. This study aims to identify whether NMES performed within the first 72 hours of septic shock diagnosis or later is safe from a metabolic perspective.This is the analysis of two randomised controlled crossover studies. Patients with acute septic shock (within the first 72 hours of diagnosis) and sepsis and septic shock in the late phase (after 72 hours of diagnosis) were eligible. Patients were submitted in a random order to the intervention protocol  and control . The patients were allocated in group 1 (intervention and control) or group 2 (control and intervention) with a wash-out period of 4 to 6 hours. Metabolic variables were evaluated by indirect calorimetry.2) values in the acute phase of septic shock when the baseline period, intervention, and control protocols were compared . The same was observed when the VO2 values in the late phase were compared . The other metabolic variables followed the same pattern, with no significant differences between the protocols. When metabolic variables were compared between acute to late phase, significant differences were observed (p<0.05).Sixteen patients were analysed in the acute septic shock study and 21 in the late sepsis/septic shock study. There were no significant differences between Oxygen Consumption . Septic shock is a life-threatening circulatory failure with inadequate tissue perfusion and altered metabolism. During septic shock, the metabolic rate increases, as evidenced by oxygen consumption which increases by 30% compared to normal basal metabolism . PatientEarly mobilisation applied to critically ill patients may improve muscle strength and functional status, as well as increases days alive and out of the hospital in the medium to long term . NeuromuAlthough some studies have found no difference when NMES was compared with usual care –6, Miao https://clinicaltrials.gov/). The protocol was published previously [The present study analyses two randomised controlled crossover studies   and wereTwo different populations were evaluated: septic shock patients in the acute phase (within the first 72 hours of diagnosis), and septic and septic shock patients in the late phase (after 72 hours of diagnosis). According to The Third International Consensus Definition for Sepsis and Septic Shock, sepsis was defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. Organ dysfunction was identified as an acute change in total SOFA score ≥2 points consequent to infection. Septic shock was identified as a clinical construct of sepsis with persisting hypotension requiring vasopressors to maintain MAP ≥65 mm Hg and having a serum lactate level >2 mmol/L despite adequate volume resuscitation –17.Patients should have stable hemodynamic conditions after fluid resuscitation, vasoactive drugs, and mechanical ventilation. Concerning the population with sepsis and septic shock vasoactive drugs were not required.Exclusion criteria were patients aged 18 years or less or over 85 years, pregnant women, those with brain death orneuromuscular diseases, or using a pre-existing neuromuscular blocker in the last 24 hours.2; major lower extremity oedema; agitation; and/or signs of pain during the electrical stimulation.NMES use contraindications included fractures; burns; skin lesions; systemic vascular impairment diseases such as systemic lupus erythematosus, thromboembolic disease, deep vein thrombosis ; lower limb amputations; cardiac pacemaker; thrombocytopenia below 20,000/mm3; body mass index above 35 kg/m2O; and 10% reduction in peripheral oxygen saturation (SpO2) baseline value or < 88% for more than 1 minute.Contraindications to begin or continue NMES procedure included the following: mean arterial blood pressure below 65 mm Hg; use of vasopressor >50% of the maximum dose ; heart rate <50 or >140 bpm; arrhythmias with hemodynamic consequences; myocardial ischemia; temperature <34°C or >39°C; intracranial pressure >20 cmH2 > 0.6 and the presence of chest tubes.Contraindications to indirect calorimetry were the need for FiOAll patients with a diagnosis of septic shock and sepsis were recruited at admission to the intensive care unit. An explanatory statement was given, and informed written consent was obtained before the commencement of the study. The author AFL had the final approval of a patient’s eligibility for the study. When patient relatives accepted the invitation to participate in the study, they signed the informed consent form.After the consent, AFL evaluated and determined the eligibility of patients. Afterwards, a nurse who was not involved in the assessment and interventions randomly allocated the participants in 1 of the 2 groups through simple randomisation (random numbers generated by the computer).The allocation sequence was hidden by ABF through sequential numbered opaque sealed envelopes. After this step, AFL opened the envelope and started the protocol.Patients were randomly submitted to the intervention protocol  and control . The patients were allocated in Group 1 (intervention and control) or group 2 (control and intervention) with a wash-out period of 4 to 6 hours.2), Energy Expenditure (EE), Carbon Dioxide Production (VCO2), and Respiratory Quotient (RQ).- Oxygen Consumption  is a non-invasive method to measure heat content generated by the entire body according to substrate utilisation. The data provided by calorimetry are EE calculated from the amounts of VO2/5% CO2) were calibrated according to the manufacturer’s instructions.Patients were submitted to IC during baseline, intervention and control protocols. The IC was measured by a portable calorimeter DELTATRAC II Metabolic Monitor  connected to a mechanical ventilator  for 30 minutes in a stable condition without manipulation of the upper airways or changes in the ventilator settings. The ICU staff is trained in how to proceed when the IC is running. We considered as steady-state to be the point after 5 consecutive minutes measurement when oxygen consumption and carbon dioxide production varied by ±10%. This technique has already been employed in some previous studies –20 and vThe endothelial progenitor cells were collected, as described in the published protocol . HoweverThe baseline period was defined as the initial moment without intervention.Intervention protocol–dorsal decubitus position with the limbs raised and NMES. The patient was placed in a dorsal decubitus position on a bed with a headboard inclined at 30°, with lower limbs raised at 20°. Adhesive electrodes (90 x50 mm) were positioned in the gastrocnemius muscle, which.was cleared, and shaved when needed. A neuromuscular electrical stimulator Neurodyn II,Ibramed, Sao Paulo, Brazil) was used to provide symmetrical biphasic pulses of 250msec at 50Hz, 2 seconds ON (a 1-second rise time and a 1-second decay time), and 5 seconds of rest during 30 minutes at an intensity to generate visible muscle contractions and articular movements.Control protocol–dorsal decubitus position with the limbs raised and without NMES. The patient was positioned as in the intervention protocol .2 baseline value or <88%, and pain evaluated by the Brazilian version of the Behavioural Pain Rating Scale during NMES session [An adverse event was defined as any change in mean arterial blood pressure (MAP) below 65mm Hg, heart rate <50 or >140 bpm, arrhythmias with hemodynamic consequences, myocardial ischemia, 10% reduction in SpO session .Data were summarised as mean and standard deviation for continuous variables, the median for ordinal variables, and frequencies and percentages for categorical variables. Data normality was verified by using the Shapiro-Wilk test. Differences between the protocols were assessed using analysis of variance ANOVA, by Friedman or Mann-Whitney tests. The p-value was used to check whether the protocol had an effect. The level of significance was set at 5%.Statistical analysis was performed using Statistical Package for Social Sciences (SPSS), version 22.0 (IBM).2 by indirect calorimetry, 10 patients were needed. We have decided to interrupt our study with 16 and 21 septic patients (<72h) and sepsis/septic shock (>72h), respectively [A new sample calculation was required to contemplate the metabolic variables. Based on Collings et al., who also evaluated VOectively .Eighty-four patients were considered eligible for the study. Of these, 43 were excluded. Thus, a total of 41 patients were randomised, sixteen in septic shock within the first 72 hours of diagnosis and 21 in sepsis and septic shock in a period longer than 72 hours of diagnosis. After randomisation, in period 1 (before wash-out), 4 patients were excluded due to non-synchronisation between the calorimeter and mechanical ventilator. In period 2 (post-wash-out), no patient was excluded, and the study ended with 16 patients in septic shock < 72 hours and 21 patients in sepsis and septic shock > 72 hours. All patients in the acute phase were diagnosed with septic shock. Some of them who had more than one diagnosis was categorised as major surgery post-operative (37%), only septic shock (31%), neoplasms (19%), and respiratory failure (13%). The focus of septic shock was abdominal (50%), pulmonary (25%), cutaneous (13%), as well as urinary and central nervous system (6% each). The main admission diagnoses of late-phase patients were a respiratory failure (33%), septic shock, sepsis and neurological diseases (14% each), post major surgery and neoplasm (10% each), and only in one patient (5%) exogenous intoxication.The SAPS 3 score in acute patients was 82.54 ± 19.21, SOFA 11 ± 3.17, and ICU mortality 38%. On the other hand, in late-stage sepsis and septic shock patients, SAPS 3, SOFA, and ICU mortality were 75 ± 17.39, 8 ± 4.04, and 23%, respectively.On the day of the intervention, median ICU stay and mechanical ventilation days for patients assessed in the first 72 hours of shock were 1.5 and 2, respectively, while for the remaining patients they were 6 and 6.5, respectively.Thirty-one per cent of septic shock patients (<72h) were using corticosteroids, most (94%) on sedation and all receiving catecholamines. Unlike patients in sepsis and septic shock (>72h), 38% of the patients were in use of corticosteroids, 62% sedated, and only 33% in the use of noradrenaline. Regarding the nutrition of septic shock patients in the acute phase, 62% were without nutrition on the day of the intervention and 38% on parenteral nutrition. Baseline energy expenditure and body mass index were on average 1476.12 ± 358.78 and 28 ± 6.26, respectively. As for the late-phase patients, a few (29%) had no nutrition, while the remaining received parenteral nutrition. For these, basal energy expenditure and body mass index were on average 1420.70 ± 238.94 and 26 ± 5.57, respectively.When intergroup comparison was made , oxygen consumption and energy expenditure showed significantly higher values and, in all protocols , patients diagnosed in the acute phase were observed. Lower RQ values were observed in septic shock patients in the acute phase .Only one patient demonstrated pain during the NMES, which was attenuated when the intensity was decreased.This study did not show significant metabolic changes when NMES was applied in the acute and late phase of septic shock patients. Early mobilisation is often delayed given the severity of the disease the patient suffers from. NMES may be a safe alternative to muscle contraction and mobilisation due to the low metabolic demand.Hickmann et al. studied 21 septic shock patients and found that manual mobilisation and passive/active cycling therapy are safe and preserve muscle fibre . NeverthWe also demonstrated that the acuteness of illness should be considered. The baseline metabolic rates were significantly higher in patients with acute septic shock than in those in the late phase. These results are consistent with the flow phase described by Cuthbertson in 1942 . An ebb Little is known about the effects of NMES on septic shock patients within the first 72h. A Danish group investigated the effects of electrical stimulation on muscle trophism in patients with acute septic shock and found a significant decrease in quadriceps volume as early as the first week after admission. However, this loss of muscle mass was not counterbalanced by electrical stimulation. Nevertheless, it is worth saying that the authors evaluated only a single leg in eight men .On the other hand, the effects of NMS on septic patients have been a little further explored. Som studies have demonstrated that the benefits might be beyond only muscle strength increase. NMES has been reported to improve muscle microcirculation and even to restore endothelial function , 28.In addition to these specific studies on sepsis, a systematic review with meta-analysis has not shown differences between NMES plus usual care and usual care alone in terms of muscle strength, ICU mortality, MV duration, or ICU stay length. Although some of the patients analysed were diagnosed with sepsis or septic shock, we cannot say whether the outcomes were better or worse. NMES meta-analysis is generally affected by the high heterogeneity of measurements and methods with different muscle groups and device settings .Likewise, Fossat et al. conducted an elegant trial enrolling critically ill adult patients, among which some septic and non-septic. The authors applied an in-bed leg cycling plus electrical stimulation. The stimulation group did not differ from the usual care although muscle strength by MRC score may have achieved a ceiling effect . FurtherOur study has some limitations. The first is the difficulty in recruiting stable septic shock patients with an adequate dosage of vasopressors and within the first 72 hours of septic shock diagnosis. Given this fact and due to COVID-19 pandemic of, we decided to end the study. The second is that we planned to analyse endothelial progenitor cells, but we could reproduce the cell analysis-based method of Stefanou et al. .Based on this finding, we could collaborate with exercise prescription decision making. The principle “one size does not fit all” has to be taken into consideration. Increased demand for physical activities in the acute septic shock phase seems to be unreasonable, as they already have high metabolic rates. Rehabilitation prescriptions for critically ill patients must be individually tailored according to illness, acuteness, and limitations.As this study has only a small part of safety evaluations, further mobilisation-related investigations in septic shock patients during the first days of diagnosis are still needed to help clinicians mobilise these patients safely as soon as possible.A further increase in metabolic demand is not desirable during the acute phase of septic shock. As there were no metabolic changes during NMES, we can conclude that NMES in patients with septic shock, either within the first 72 hours of diagnosis or later, is safe in a metabolic view.S1 Checklist(DOC)Click here for additional data file.S1 Table2- Oxygen consumption, EE- Energy Expenditure; VCO2-Carbon Dioxide Production; RQ: Respiratory Quotient.VO(DOCX)Click here for additional data file.S2 Table2- Oxygen consumption, EE- Energy Expenditure; VCO2-Carbon Dioxide Production; RQ: Respiratory Quotient.VO(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


Antimicrobial resistance (AMR) is a serious threat to global public health. WHO emphasises the need for countries to monitor antibiotic consumption to combat AMR. Many low-income and middle-income countries (LMICs) lack surveillance capacity; we aimed to use multiple data sources and statistical models to estimate global antibiotic consumption.In this spatial modelling study, we used individual-level data from household surveys to inform a Bayesian geostatistical model of antibiotic usage in children (aged <5 years) with lower respiratory tract infections in LMICs. Antibiotic consumption data were obtained from multiple sources, including IQVIA, WHO, and the European Surveillance of Antimicrobial Consumption Network (ESAC-Net). The estimates of the antibiotic usage model were used alongside sociodemographic and health covariates to inform a model of total antibiotic consumption in LMICs. This was combined with a single model of antibiotic consumption in high-income countries to produce estimates of antibiotic consumption covering 204 countries and 19 years.We analysed 209 surveys done between 2000 and 2018, covering 284 045 children with lower respiratory tract infections. We identified large national and subnational variations of antibiotic usage in LMICs, with the lowest levels estimated in sub-Saharan Africa and the highest in eastern Europe and central Asia. We estimated a global antibiotic consumption rate of 14·3  defined daily doses (DDD) per 1000 population per day in 2018 (40·2 [37·2–43·7] billion DDD), an increase of 46% from 9·8 (9·2–10·5) DDD per 1000 per day in 2000. We identified large spatial disparities, with antibiotic consumption rates varying from 5·0 (4·8–5·3) DDD per 1000 per day in the Philippines to 45·9 DDD per 1000 per day in Greece in 2018. Additionally, we present trends in consumption of different classes of antibiotics for selected Global Burden of Disease study regions using the IQVIA, WHO, and ESAC-net input data. We identified large increases in the consumption of fluoroquinolones and third-generation cephalosporins in North Africa and Middle East, and south Asia.To our knowledge, this is the first study that incorporates antibiotic usage and consumption data and uses geostatistical modelling techniques to estimate antibiotic consumption for 204 countries from 2000 to 2018. Our analysis identifies both high rates of antibiotic consumption and a lack of access to antibiotics, providing a benchmark for future interventions.Fleming Fund, UK Department of Health and Social Care; Wellcome Trust; and Bill & Melinda Gates Foundation. Bacterial infections are a major cause of morbidity and mortality worldwide. Antibiotics have been hugely successful in improving health outcomes, and alongside improvements in nutrition, clean water, sanitation, and vaccination provision, have aided in the global reduction of under-5 mortality from 216 deaths per 1000 livebirths in 1950 to 39 deaths per 1000 livebirths in 2017, and an increase in male life expectancy from 48 years to 71 years within the same time period.4The relationship between antibiotic use and the development and spread of AMR is well documented.Sustainable Development Goal 3.8 specifies the need for “access to safe, effective, quality and affordable essential medicines and vaccines for all”.11Evidence before this studyDespite some excellent national and regional surveillance systems for antimicrobial consumption, mostly in high-income countries (HICs), large data gaps remain. This is compounded by the fact that the most comprehensive and detailed sources of global antibiotic sales data are proprietary databases. The IQVIA MIDAS database covers 76 countries or territories, including only one low-income country. To identify existing studies estimating global antibiotic consumption we searched PubMed from Jan 1, 1980, to July 28, 2020, for studies using the terms  AND (“consumption” OR “use” OR “usage”) AND  in the title, with no language restrictions.We identified five studies that estimated global antibiotic consumption, four of these were published in the past 10 years and were based entirely on the IQVIA MIDAS database, with the two most recent studies following the WHO Anatomical Therapeutic Chemical (ATC) classification/defined daily doses (DDD) methodology. Additionally, several studies were identified that focused on analysing the consumption of child-appropriate oral formulations of antibiotics using the IQVIA MIDAS database. The most comprehensive study estimated global antibiotic consumption from 2000 to 2015 and calculated consumption for the 76 countries or territories covered by the dataset, extrapolating the rates of consumption to the global population. However, the study did not provide estimates of antibiotic consumption for individual countries not covered by the IQVIA dataset.Our searches for global antibiotic usage identified one study estimating caregiver-reported antibiotic usage in children (aged <5 years) with fever, diarrhoea, or cough with difficulty breathing in low-income and middle-income countries (LMICs). The study used Demographic Health Survey and Multiple Indicator Cluster household data; estimates were stratified by WHO region. The study did not produce estimates of antibiotic usage for individual countries and districts.Added value of this studyWe provide the first estimates of longitudinal human antibiotic consumption (ATC J01) for 204 countries from 2000 to 2018, using a novel approach that applied spatial modelling techniques and incorporated multiple data sources, including large-scale antibiotic usage surveys. We used the most recent version of the WHO ATC/DDD guideline and index with higher DDD values for key antibiotics such as ampicillin and amoxicillin. We estimate that the global volume of antibiotic consumption was 40·2  billion DDD in 2018, an increase of 46% since 2000. Antibiotic consumption in 2018 was inequitable between HICs and LMICs, with respective rates of 20·6 DDD and 13·1 DDD per 1000 population per day.Rates of antibiotic consumption varied by nearly a factor of 10, highlighting the lack of access to essential antibiotics as well as overuse. We also present trends in consumption of different classes of antibiotics using the IQVIA, WHO, and European Surveillance of Antimicrobial Consumption Network (ESAC-Net) input data. Additionally, we provide detailed subnational estimates of community-based antibiotic usage in children with lower respiratory tract infections in LMICs (135 countries), from 2000 to 2018. Our study highlights the substantial variation in antibiotic usage between LMICs and the high inequalities within some countries.Implications of all the available evidenceInsights from longitudinal antibiotic consumption surveillance within a country are essential for implementing and monitoring policy to tackle inappropriate antibiotic use. Addressing lack of access as well as inappropriate antibiotic use is crucial for tackling the growing threat of antimicrobial resistance and ensuring fair and equitable access to essential medicines for all.Despite some excellent national and regional surveillance systems,This study aims to estimate global antibiotic consumption using two different types of data: first, antibiotic consumption data, referring to the aggregated amount of antibiotics consumed by a country, derived from national sales and import and reimbursement databases, obtained from multiple sources; and second, antibiotic usage data, referring to the use of antibiotics by individual patients,We modelled antibiotic consumption in HICs and LMICs separately due to differing covariate effects and data availability . For LMIAntibiotic usage data were extracted from household surveys, primarily the United States Agency for International Development's Demographic Health Surveys (DHS) and UNICEF's Multiple Indicator Cluster Surveys MICS; . These lData on the proportion of children (aged <5 years) with symptoms of lower respiratory tract infection in the previous 2 weeks who received antibiotics, for the time period from 2000 to 2018, were extracted and linked to the smallest geographical location available: global positioning system cluster coordinates (point locations) or administrative districts (hereafter administrative level 1 areas will be referred to as states and administrative level 2 areas as districts). Administrative districts were resampled to point locations using population weighted K-means clusteringWe used a two-stage Bayesian hierarchical model, established for fine scale mapping of health outcomesAntibiotic usage was not estimated for North Korea or island nations with populations under 100 000 people due to a lack of sufficient survey data, covariates, and geographical contiguity. For these countries, national estimates were inferred as the median antibiotic usage for that region. Finally, we analysed the within-country variation in antibiotic usage by calculating the relative deviation of antibiotic usage in each district from the country mean , and summarising at the national level by calculating the range of these relative deviations.We obtained antibiotic consumption data from IQVIA for 76 countries and territories from 2000 to 2018. Data were provided in two datasets; 2000–13 (IQVIA MIDAS) data were provided as standard units for each product of J01 class antibiotics , and data for 2014–18  were provided in kilograms, aggregated for each J01 class antibiotic (regardless of product or preparation); we have referred to the combined data as the IQVIA dataset. Data were provided disaggregated by hospital and retail sectors.We restricted the dataset to antibiotics for systemic use . Standard units (for 2000–13) were converted to kilograms and defined daily doses (DDD) calculated for each antibiotic following the latest versions of the WHO Collaborating Centre for Drug Statistics Methodology and ATC/DDD index  countries contained only data from the retail sector; for these data points the values for antibiotic consumption in hospitals were imputed using classification and regression trees (CART) multiple imputation method from the MICE package (version 3.7.0) in R (version 3.6.1). The data for all J01 antibiotics consumed in hospital and retail sectors were aggregated to provide one value for total consumption per country year.Global Burden of Disease (GBD) study. The final dataset covered 47 (68%) of 69 HICs and 65 (48%) of 135 LMICs.Additional sources of national antibiotic consumption data from which DDD were calculable for countries and years not included in the IQVIA datasets were obtained from the WHO report on surveillance of antibiotic consumption (referred to as the WHO dataset), which contains consumption data from 65 countries for 2015–16;Data coverage for HICs was high (99·2% population coverage); countries with missing data  were genFor LMICs, data coverage was sparser than for HICs (78·3% population coverage). To account for this sparsity of data and to leverage the estimates of antibiotic usage in LMICs, we used a two-stage modelling framework to estimate antibiotic consumption (in DDD per 1000 population) for each LMIC from 2000 to 2018. The estimates of antibiotic usage prevalence, aggregated to the national level, were used as a covariate  to fit a stacked ensemble model. Stacking has been shown to improve predictive validity compared with non-ensemble modelling methodologies, to aid with covariate selection and to account for non-linearity and interactions between covariates.The estimates of antibiotic consumption in LMICs and HICs were combined to present global antibiotic consumption in DDD per 1000 population per day for each year, from 2000 to 2018. Uncertainty for the LMIC antibiotic consumption model was estimated by taking 1000 draws of the Gaussian process regression and calculating the 95% UIs. Uncertainty was not calculated for HICs due to the differing modelling strategies.A spatial-temporal modelling approach to estimate consumption by antibiotic class was deemed inappropriate, due to the lack of spatial trends and association to health and sociodemographic covariates for the data. Antibiotic consumption for each class of antibiotics (ATC level 3 and select ATC level 4 classes) and WHO AWaRe categoriesThe funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.Nuffield Department of Medicine's website. Results showed high spatial variability in our estimates of antibiotic usage, both between and within countries.From 2000 to 2018, data from 209 surveys and 284 045 children (aged <5 years), covering 101 countries and 19 years, were input into the model of antibiotic usage in LMICs , with national estimates highest in Ukraine , Kazakhstan , Bosnia and Herzegovina , and Belarus , with high usage estimated in Vietnam  and Malaysia , whereas Papua New Guinea , Philippines , and Indonesia  were notably lower.From 2000 to 2018, the prevalence of antibiotic usage was consistently low in sub-Saharan Africa and consistently high in eastern Europe and central Asia . HoweverWe identified high inequalities in antibiotic usage within countries; in 2018, the relative deviation in antibiotic usage was highest in India and Laos (both 51% relative deviation). In sub-Saharan Africa, the relative deviation in antibiotic usage was highest in Nigeria (46%) and Benin (40%). Four countries in sub-Saharan Africa  were home to the 50 districts with the lowest proportion of antibiotic usage. Overall, there was little difference in the relative deviation in antibiotic usage between 2000 and 2018; however, decreases were noted in India (73% to 51%), Indonesia (63% to 25%), and Cambodia  billion DDD in 2018, equating to a rate of 14·1 (13·2–15·6) DDD per 1000 population per day . Antibiotic consumption was inequitable between World Bank classified HICs and LMICs, with respective rates of 20·6 DDD per 1000 per day and 13·1 (95% UI 11·8–14·6) DDD per 1000 per day . South AWe identified large spatial disparities in the rates of antibiotic consumption, with the rates in Greece (45·9 DDD per 1000 per day) and Tunisia (38·0 [95% UI 36·0–40·2] DDD per 1000 per day) almost ten times higher than the lowest rates found in countries such as the Philippines (5·0 [4·8–5·3]), Honduras (5·6 [5·4–5·9]), Burundi (5·3 [4·3–6·4]), and Central African Republic  DDD per 1000 per day in 2000 to 14·3 (13·2–15·6) in 2018. This differed greatly between HICs and LMICs; although consumption rates remained stable in HICs across the study period (20·8 DDD per 1000 per day in 2000 compared to 20·6 in 2018), there was a 76% increase observed in LMICs  with lower respiratory tract infections in LMICs from 2000 to 2018. We identified large variations in the proportion of children with reported antibiotic use across and within LMICs. In 2018, the highest levels of antibiotic usage were estimated in the central Europe, eastern Europe, and central Asia super-region (median antibiotic usage of 72%), and the lowest levels for sub-Saharan Africa (median antibiotic usage 43%). Our subnational estimates highlight inequalities in antibiotic usage within countries, underlining issues of access to health care, particularly in poor and rural communities. A recent studyWe identified not only large variations in the rates of total antibiotic consumption across countries, but also in the proportion of antibiotics classes used in different geographical contexts.32The main limitations of our modelling study are centred around data quality and availability issues. Data on antibiotic usage in LMICs are sparse, with most studies focusing on the knowledge, attributes, and practices of antibiotic usage in specific study locations.Another limitation stems from the fact that we used antibiotic usage in children (aged <5 years) to model antibiotic consumption in the general population in LMICs, assuming that the spatial–temporal trends in antibiotic usage between adults and children were comparable. High antibiotic usage in adults is corroborated by health-care utilisation surveys done in LMICs that show a strong trend for adults to access community pharmacies as first point of contact and to self-medicate with over-the-counter antibiotics;56The IQVIA MIDAS database is a proprietary database, restricting access and analysis. For the 24 included countries lacking data from hospitals, we relied on multiple imputation methods to estimate total antibiotic consumption. Additionally, for some countries, such as India, the IQVIA MIDAS hospital data exclude public facilities, which could underestimate consumption. Only formal channels of antibiotic sales are included in IQVIA MIDAS; therefore sales of antibiotics from informal markets, which could make up a considerable proportion of antibiotic sales in LMICs,The lack of antibiotic consumption data in LMICs is another limitation. Our combined dataset only covers 48% of LMICs, highlighting the urgent need for the establishment of antibiotic consumption surveillance programmes,To our knowledge, our study provides the first longitudinal antibiotic consumption estimates for 204 countries from 2000 to 2018, presenting an important benchmark of antibiotic consumption and highlighting spatial disparities in antibiotic usage and consumption. We identified countries with very high antibiotic consumption, indicating excess use, and countries with very low consumption, indicative of a lack of access to essential antibiotics. These findings underline the scope and potential major benefits of antibiotic stewardship interventions, aided by antibiotic consumption surveillance,www.ndm.ac/gram-project/antibiotic-consumption.The source code for all analysis is available at https://github.com/NDM-GRAM/Antibiotic-consumption. Interactive data visualisations and all estimates, including antibiotic usage estimates aggregated to national, state, and district levels, and antibiotic consumption estimates for total J01 antibiotic consumption, consumption by ATC level 3 classes, and consumption by select ATC level 4 classes are available at AS has been the recipient of research grants from the Bill & Melinda Gates Foundation. All other authors declare no competing interests.


Diet is one of the most important modifiable risk factors for the development of type 2 diabetes. Here, we aim to identify dietary patterns and to investigate their association with prediabetes, undetected diabetes and prevalent diabetes.n = 698), prediabetes (n = 459), undetected diabetes (n = 49), and prevalent diabetes (n = 99). Dietary patterns were identified through principal component analysis followed by hierarchical clustering. The association between dietary patterns and glucose tolerance status was investigated using multinomial logistic regression models.The present study included 1305 participants of the cross-sectional population-based KORA FF4 study. Oral glucose tolerance test (OGTT) measurements together with a physician-confirmed diagnosis allowed for an accurate categorization of the participants according to their glucose tolerance status into normal glucose tolerance , undetected diabetes  or prevalent diabetes , compared to participants following the Prudent pattern.To our knowledge, the present study is one of the few investigating the association between dietary patterns and prediabetes or undetected diabetes. The use of a reference group exclusively including participants with normal glucose tolerance might explain the strong associations observed in our study. These results suggest a very important role of dietary habits in the prevention of prediabetes and type 2 diabetes.The online version of this article (10.1007/s00394-020-02416-9) contains supplementary material, which is available to authorized users. Type 2 diabetes is one of the leading causes of morbidity and mortality in the developed world, representing a major public health issue and placing an heavy and increasing financial burden to the health-care system of different countries , 2. The In addition to insufficient physical activity, unhealthy diet is considered as one of the most important modifiable risk factors for the development of type 2 diabetes , 2. TradThe association between dietary patterns and the risk of type 2 diabetes has been previously investigated in several studies –13. A WeUsing data from the Cooperative Health Research in the Region of Augsburg (KORA) FF4 study, our group previously identified significant associations between single food groups and prediabetes, undetected diabetes and prevalent diabetes . To exteThe findings of the present study were reported according to standards of the “Strengthening the Reporting of Observational Studies in Epidemiology—Nutritional Epidemiology (STROBE-nut)” checklist .n = 6), unclear glucose tolerance status due to missing OGTT information (n = 93), missing dietary information (n = 638) and missing covariates (n = 2) were excluded from the present analyses. Additionally, participants with a diagnosis of cardiovascular disease (n = 82) or cancer (n = 153) were excluded, since having a severe disease may have led to changes in dietary behavior. A total of 1305 participants aged 38–87 years were eventually included in the present analyses. The characteristics of participants with and without dietary information are presented in Table S1.The analyses of the present study were performed using data from the cross-sectional population-based KORA FF4 study, which was conducted in the region of Augsburg in Southern Germany in 2013/2014. This is the second follow-up of the KORA S4 health survey, which was conducted between 1999 and 2001. Detailed information about the participation response has been previously published . BrieflyThe KORA FF4 study was approved by the Ethics Committee of the Bavarian Chamber of Physicians and all procedures followed the ethical standards of the Declaration of Helsinki. All participants provided written informed consent.Prevalent diabetes  was defined by either self-reported diagnosis of type 2 diabetes or use of antidiabetic medication. The diagnosis was then further confirmed by the participant’s physician. All participants without a diagnosis of type 2 diabetes underwent a standard OGTT. The tests were conducted in the morning and participants were asked to fast for 10 h before the test, not to perform any heavy physical activity on the previous day and not to smoke before or during the test. Additionally, the OGTT were not performed in participants with medical contraindications. Fasting venous blood was collected for glucose measurement using serum tubes, before and 2 h after intake of 75 g of anhydrous glucose . Serum glucose was analyzed using a hexokinase method  .Participants were categorized by glucose tolerance status according to the diagnostic criteria of the American Diabetes Association (ADA) . Normal The dietary assessment of the KORA FF4 study consisted of up to three repeated 24-h food lists (24HFL) and one food frequency questionnaire (FFQ). A total of 1602 individuals completed at least one 24HFL and one FFQ. Of these individuals, 652 (40.7%) completed two and 826 (51.6%) completed three 24HFL. As closed lists, the 24HFL included > 300 food items and were used to assess food consumption over the past 24 h. Additionally, the 24HFL also included information about intake of dietary supplements .Habitual food intake was computed using an advanced blended two-step approach as described in detail elsewhere , and furn = 34) were considered as supplementary individuals for the principal component analysis . In the present analysis, seven principal components were retained based on multiple criteria, including eigenvalues > 1, scree plot and total explained variance . The retained principal components were subsequently used as input to hierarchical clustering using the Ward’s criterion [n to n + 1 cluster  as well as partition interpretability. Two clusters were eventually retained for interpretation. The stability of the dietary patterns was further tested by using five principal components as input for hierarchical clustering. Very similar clusters were obtained.Dietary patterns were identified through principal component analysis and clustering in a two-step approach, analogously to Krieger and colleagues . First, riterion , and the2), blood pressure (mmHg), total cholesterol (mmol/l) and triglycerides (mmol/l) were measured at the study center by trained personnel according to international standard protocols. Waist circumference was measured to the nearest 0.1 cm at the minimum abdominal girth. Body weight and body height, used to calculate the BMI, were measured in light clothing to the nearest 0.1 kg and 0.5 cm, respectively. Finally, measurements of blood pressure were performed three times with participants in a sitting position using an automatic and validated device [Potential covariates were selected according to the previous literature on the association between diet and type 2 diabetes , 28. Aged device .Descriptive statistics were used to characterize the KORA FF4 participants overall and by dietary patterns. Odds ratio (OR) and 95% confidence intervals (CI) were calculated by multinomial logistic regression models to investigate the association between dietary patterns and glucose tolerance status . Three different regression models were fitted. Model 1 was adjusted for age and sex. Model 2 was further adjusted for marital status, education, physical activity and smoking. Marital status was added as adjusting factor in the regression models because of the significant associations with both diet and health outcomes observed in previous studies , 35. FinFactoMineR package [nnet package [All statistical analyses were conducted with R software (version 3.5.3 for Windows). Principal component analysis and hierarchical clustering were performed using the  package , whereas package . Statistn = 707, 54.2%), while the second pattern, labeled Western pattern, was characterized by high consumption of processed meat, alcoholic beverages, red meat, refined grains and sugar-sweetened beverages .Using principal component analysis and hierarchical clustering, we were able to identify two distinct dietary patterns among KORA FF4 participants, collectively explaining 52.7% of the total variation in diet Fig. . The fir2 and a waist circumference of 99.5 cm in men and of 89.1 cm in women were observed among participants following the Prudent pattern, whereas 78.6% males, a BMI of 28.5 kg/m2 and a waist circumference of 103.0 cm in men and of 93.7 cm in women were observed among participants following the Western patterns. In addition, participants following the Prudent pattern were more likely to be older, physically active and never smokers.The characteristics of the KORA FF4 participants overall and by dietary patterns are presented in Table Table To highlight the difference in results obtained when using OGTT measurements for glucose tolerance status and to enhance comparability with previous published studies, the association between dietary patterns and glucose tolerance status was also investigated considering participants with normal glucose tolerance, prediabetes and undetected diabetes altogether as reference group Table . OverallUsing data from the KORA FF4 study, we identified two distinct dietary patterns, a Prudent pattern and a Western pattern. Strong significant associations were observed between dietary patterns and glucose tolerance status. In fact, participants following the Western pattern, characterized by high consumption of red and processed meat, alcoholic beverages, refined grains and sugar-sweetened beverages, had significantly higher chances of having prediabetes, undetected diabetes and prevalent diabetes, compared to participants following the Prudent pattern.To our knowledge, the present study is one of the few investigating the association between dietary patterns and prediabetes or undetected diabetes in a Western population , 15. We As expected, the association between dietary patterns and undetected diabetes observed in the present study was stronger compared to the association between dietary patterns and prevalent diabetes. This is likely due to reverse causation, since participants with prevalent diabetes were aware of the diagnosis and could have changed to a healthier dietary behavior before the beginning of the study. In contrast, the dietary habits of participants with undetected diabetes were more likely to remain unchanged, resulting in a potentially less biased estimate of the association between diet and type 2 diabetes.Moreover, in all analyses, the association between dietary patterns and glucose tolerance status was attenuated after adjustment for waist circumference, hypertension, total cholesterol and triglycerides. However, since the association between diet and type 2 diabetes is likely to be mediated by these factors, adjustment for these variables may have led to an underestimation of the true effect. Unfortunately, due to the cross-sectional design of the study, the potential mediation effect of the above-mentioned variables could not be investigated in the present analyses.The strong positive association between the Western pattern and glucose tolerance status observed in the present study may reflect a joint effect of single food groups. In fact, most of the food groups extensively consumed in the Western pattern were also associated with type 2 diabetes in our previous study , and in Despite the use of different methods, the dietary patterns identified in the present analysis share similar characteristics with dietary patterns identified in other Western populations, and our results are in line with previous studies. Using data of the large Nurses’ Health Study, Fung et al. observed a relative risk (RR) for extreme quintiles of 1.49 (95% CI 1.26–1.76) for a Western pattern generated by factor analysis , whereasThe associations between dietary patterns and type 2 diabetes observed in the current study  were stronger compared to most of the results in the previous literature. This might be due to the use of a reference group, which exclusively included participants with normal glucose tolerance. As mentioned before, the present study is one of the few investigating the association of dietary patterns with not only prevalent type 2 diabetes, but also prediabetes and undetected diabetes in a Western population , 15. In Few previous studies also investigated the association between dietary patterns and prediabetes , 16–18. To enhance comparability between our study and the above-mentioned studies, we also conducted a further analysis, where individuals with normal glucose tolerance, prediabetes and undetected diabetes were considered altogether as the reference group. When adjusting for age, sex, marital status, education, physical activity and smoking, the analysis revealed an OR of 2.06 (95% CI 1.14–3.75) for prevalent diabetes among participants following the Western pattern compared to participants following the Prudent pattern. This result is very similar to the results of large cohort studies mentioned before, supporting the strong association observed in the main analysis of the present study. Further studies investigating the association between diet and type 2 diabetes should therefore consider a more accurate categorization of diabetes patients according to their glucose tolerance status whenever possible.Major strengths of our study are the large sample size, which was originally randomly selected from the general population and had a high participation rate, the inclusion of a large variety of food items, the use of a sophisticated method to calculate dietary intake and the data collection by trained personnel. In addition, collection of OGTT measurements together with a physician-confirmed diagnosis allowed for an accurate categorization of the participants according to their glucose tolerance status and for the use of a reference group, which exclusively included participants with normal glucose tolerance. Another strength is the use of a two-step approach to derive dietary patterns. Like few others, we identified dietary patterns using principal component analysis followed by hierarchical clustering , 47, 48.The present study has also some limitations. First, because of the cross-sectional design of the study, our results do not allow for drawing conclusions about the causal relationship between dietary patterns and glucose tolerance status. However, since participants with undetected diabetes were not aware of the diagnosis before the beginning of the study, it is unlikely that reverse causation influenced this association. Second, individuals who agreed to participate in the KORA FF4 study were probably more health-conscious compared to participants of the original KORA S4 health survey, potentially leading to participation bias. Also, under- or over-reporting and recall bias in dietary questionnaires cannot be excluded. Due to incomplete dietary assessment, a substantial part of the KORA FF4 study is lacking habitual dietary intake data. However, when comparing the characteristics of participants with and without dietary information, a high similarity between groups was found, also with respect to glucose tolerance status (Table S1). The methods used to identify dietary patterns have also some limitations. In fact, both principal component analysis and clustering methods partially involve subjective analytical decisions  and may therefore show limited stability or reproducibility . HoweverTo conclude, a Prudent pattern and a Western pattern were identified among participants of the KORA FF4 study. The availability of OGTT measurements together with a physician-confirmed diagnosis allowed for an accurate categorization of the participants according to their glucose tolerance status. Strong, positive associations were observed between the Western pattern, characterized by high consumption of red and processed meat, alcoholic beverages, refined grains and sugar-sweetened beverages, and prediabetes, undetected diabetes or prevalent diabetes. The associations observed in the present study were stronger compared to the ones observed in our previous study, where diet was investigated using single food groups, suggesting that dietary patterns may be superior to individual food items for analyzing the association between diet and type 2 diabetes. These results suggest an important role of dietary habits in the prevention of prediabetes and type 2 diabetes, and may help to develop dietary recommendations that could serve as a basis for effective public health interventions, targeting not only individuals with type 2 diabetes, but also those at high risk of developing type 2 diabetes.Supplementary file1 (PDF 253 kb)Below is the link to the electronic supplementary material.


Dentalfluorosis is caused by prolonged exposure to excessive fluoride during the period of permanent tooth formation and is characterized by tooth discoloration, pitting, and loss of shape. Communities living near Lake Kivu in Western Rwanda exhibit a high prevalence of dental fluorosis; however, data on prevalence and risk factors are scarce.This cross sectional, quantitative study used a One Health approach to investigate dental fluorosis prevalence among people and livestock and to measure fluoride content in the environment. In 2018, oral health examinations were conducted to assess the prevalence of fluorosis in children (aged 9 to 15 years), cattle and goats residing on Gihaya Island . All children and cattle/goats meeting basic eligibility criteria  were invited to participate. Presence and severity of dental fluorosis was categorized according to the Dean’s Fluorosis Index. Samples of local foods, water, soil and grass were collected from communal sources and individual households and analyzed for fluoride content using standard laboratory techniques. Descriptive and binomial analyses (Fisher Exact Test) were used to assess this dataset.Overall, 186 children and 85 livestock owners (providing data of 125 livestock -23 cattle and 102 goats) participated. Dental fluorosis was recorded in 90.7% of children and 76% of livestock. Moderate to severe fluorosis was observed in 77% children while goats and cattle most often exhibited mild or absent/questionable severity, respectively. Water from Lake Kivu (used primarily for human cooking water and livestock drinking water) contained fluoride levels that were consistently higher than the maximum threshold (1.5 mg/L) recommended by the World Health Organization. Other sources (borehole and rainwater) were within safe limits. All food, soil and grass samples contained fluoride. The highest levels were observed in porridge (0.5 mg/g) and small fishes (1.05 mg/g).Altogether, dental fluorosis was highly prevalent among children and goats on Gihaya Island with various food and water sources contributing a cumulative exposure to fluoride. An immediate and coordinated response across human, animal and water professionals is needed to reduce fluoride exposure within safe limits for island residents.The online version contains supplementary material available at 10.1186/s42522-021-00054-7. Fluoride is a naturally occurring element that contributes to the formation of mineralized structures such as teeth and bone . ExcessiBos taurus), buffalo , and goats (Capra aegagrus hircus) [Water is the most important source of dietary fluoride in people, accounting for 75–90% of daily intake . At mode hircus) , 3. High hircus) . Some fo hircus) –15. Risk hircus) .Excessive fluoride exposure during early growth and development influences the extent and impact of dental fluorosis. Children between one and four years of age are at the highest risk for dental fluorosis in their permanent anterior teeth . After eIn 2017, the National Oral Health Survey of Rwanda conducted country-wide oral health examinations and determined that 93 of the 126 identified dental fluorosis cases lived on Gihaya Island in the Western Province of Rwanda . Data peGihaya Island is a small island situated in Lake Kivu between the Democratic Republic of Congo and Rusizi District in Western Rwanda. The ~ 1,230 human residents are almost exclusively engaged in subsistence agricultural activities, growing staple crops, harvesting small fish, and raising livestock  to feed their families and earn income. Three boreholes exist but only one solar-powered water pump was operational at the time of this study. Basic health services can be accessed through Community Health Workers and a local health post. There are no oral health services on the island.Data for this study were obtained through (1) interviewer-administered surveys of children and livestock owners, (2) oral examinations of children and livestock, and (3) food, water, and environmental sample collection and analysis. Inclusion criteria for children were ages 9–15 years, residence on Gihaya Island since birth, upper and lower anterior permanent central and lateral incisors fully erupted, and parental permission to participate in the study. For inclusion of livestock data, owners were required to be 18 years or above. Goats and cattle were required to be over six months of age, have resided on the Gihaya Island since birth and able be handled safely . The inclusion criteria were developed to allow for the maximum number of permanent teeth in children and animals who were lifelong residents of the Island. All eligible children and livestock living on this island were invited to participate. Surveys, oral health assessments and initial food sampling were conducted over a four-day period in December of 2018. Community residents were informed of the study by local Community Health Workers prior to and on the day of data collection. All eligible children and livestock owners (with their livestock) were invited to participate. Surveys and oral examination of children were conducted at the local primary school while livestock owners were met at their homes. Based on high levels of human dental fluorosis and water fluoride content observed in 2018 (as reported in this study), a second research team returned to the island in August of 2019 to collect additional water and food samples for fluoride and microbial analyses, and to measure assess dietary habits . To our knowledge, there were no relevant social or environmental changes impacting natural water sources during the time interval.The questionnaire for children addressed demographics, water sources, diet, and satisfaction with tooth appearance  . The preInitial water samples (2018) were obtained near the lake shore where residents collected cooking and drinking water, from the one functional borehole and from the local health post rainwater collection tank. Three samples were collected at each location using standard collection techniques for wading. Samples from each location were tested in triplicate with the fluoride testing probe at the University of Rwanda laboratory in Kigali. Samples of locally sourced and commonly eaten uncooked foods  were collected from island residents. Soil and grass samples were collected from two livestock grazing points on the island. One sample of each food, grass and soil type were collected. In 2019, three additional water samples were collected from each of the following water sources: six locations around the island where people gather water for drinking and cooking from Lake Kivu, a water storage tank from a solar-powered borehole pump, and a rainwater catchment tank at the local health post. Various prepared foods  were collected from two households, which had cooked foods with water from Lake Kivu according to their norm. All water, food, soil, and grass samples were collected under the supervision of a certified lab technician and stored in sterile 100 mL plastic bottles or plastic zip lock bags. Samples were transported in cooled containers and stored in refrigerators until analyzed.Food and soil samples were dried in an oven at 105 °C for 12 – 24 h. Dried sample were crushed into a fine powder with a mortar and pestle. For each sample, 1 g of powder was placed in a crucible and covered with 5 mL of NaOH 8 M solution. Crucibles were heated on a hot plate for a minimum of 5 min and then placed in an 200 °C oven for 16 h. After 16 h, crucibles were cooled to room temperature and then rehydrated in 15 mL distilled water. Solutions were gently shaken until homogenous, neutralized with 37% hydrochloric acid to reach pH 7.2 – 7.5, and then diluted in 25 mL distilled water before measuring the fluoride concentration.Fluoride concentrations were measured using an pH/ISE ion selective electrode, with a fluoride probe (model ISEF12101) from HACH , according to the user manual instructions . The flup-value of < 0.05 was considered statistically significant.Questionnaire data were uploaded from the KOBO platform and analyzed using Statistical Package for the Social Sciences (SPSS), version 25 . Descriptive statistics, including counts and percentages for categorical variables and means and standard deviations (SDs) for continuous variables, were calculated. The Fisher Exact Test was used to identify associations between categorical clinical outcomes and demographic variables of interest. A Overall, 186 children (100% of eligible residents) participated in this study; 47.3% of the children were male while 52.5% were female  between goats (89.2%) and cattle (17.3%). Only mild fluorosis was present in cattle. Mild and moderate fluorosis was present in goats.Nearly all (90.7%) children had signs of dental fluorosis, with most (77%) categorized as moderate to severe Fig. . There wMost (95.7%) children stated that their households used the solar powered borehole as the main source of drinking water. Three-quarters 74.2%) reported Lake Kivu as the main household source of cooking water  and highest (1.05 mg/g) in small fishes  were higher than borehole water 0.13 mg/L) or rainwater  and goats (N = 563) to be 59 and 12%, respectively, where mean fluoride concentrations in water exceeded 1.5 mg/L [The prevalence of dental fluorosis among Gihaya Island children (90.7%) was higher than the national average (6%)  but was 1.5 mg/L . These l1.5 mg/L , 35.Gihaya Island children and livestock owners both reported Lake Kivu as a primary water source for cooking and livestock. However, fluoride levels in Lake Kivu freshwater were far higher than groundwater and rainwater, especially when boiled, and exceeded the WHO recommended threshold level. Subsequent microbial analysis indicated intermediate to very high risk of Lake Kivu samples due to high coliform contamination. Therefore, Gihaya Island residents who boil their water as a measure to avoid diseases caused by waterborne pathogens also increase their exposure to waterborne fluoride. Overall, rainwater and groundwater appeared to be safer choices for drinking and cooking water, but access became limited during the study period. Access to the sole rainwater catchment tank is restricted to health post use and the solar-powered borehole pump changed from a free to a paid service that is not affordable or geographically convenient for all residents. A campaign to sensitize residents fluorosis etiology should emphasize the importance of safe water for overall household health. Obtaining support to improve access to safe water is essential for the health of the community. Short-term, this could include rehabilitation of the two non-functioning boreholes, reducing the price of the solar pump borehole water or investing in de-fluoridation systems. Long-term interventions could include introducing piped chlorinated water from the mainland or installing rainwater catchment systems.Our analysis of foods commonly consumed on Gihaya Island suggested that raw fishes and cooked porridge contained the highest levels of fluoride. This is not surprising given that fish bioaccumulate chemicals from their surrounding water and porridge is often prepared by boiling cereals in Lake Kivu water. Some commercially processed foods, such as infant formula, can contain high levels of fluoride. The concentration of fluoride would increase further when prepared with water containing high fluoride levels , 37. TheDental fluorosis is known to adversely impact the quality of life of those affected . Nearly This pilot One Health study evaluated fluorosis among all children and livestock meeting the inclusion criteria, and comprehensively evaluated fluoride sources in common food and water sources. The short study duration and use of single examiners for human and livestock oral exams provided consistency in the fluorosis assessments. However, self-reported survey data may have been affected by recall bias or a desire for social acceptability. We developed a modified Dean’s Fluorosis Index to facilitate the assessment of the severity of dental fluorosis in cattle and goats. While helpful in standardizing animal fluorosis scores, it was not pretested and is a limitation of the study design. Additional information on adult fluorosis prevalence, household diet, and socioeconomic factors would provide a more comprehensive picture of this issue and permit calculation of fluoride exposure through various food and water sources. Even with these limitations, the large sample size in relation to the population provides invaluable foundational information regarding the human, animal, and environmental aspects of fluorosis on Gihaya Island.This study documented high levels of dental fluorosis in people and goats on a remote island in Rwanda and identified possible sources of environmental fluoride exposure. The physical and emotional consequences of dental fluorosis can be serious, especially in a marginalized community already affected by food scarcity, poverty, and high levels of infectious disease. A multidisciplinary and holistic approach, including oral health practitioners, veterinarians, physicians, water specialists and social scientists is needed to develop affordable, sustainable, and evidenced-based solutions for reducing the burden of fluorosis on Gihaya Island. This includes identifying safe and accessible drinking water sources, treatment for those already affected, and improving public awareness of prevention practices.Additional file 1. Dental Fluorosis Participant SurveyAdditional file 2. Dental Fluorosis Livestock Owner Survey


The need for temperature modulation (mostly cooling) in critically ill patients is based on the expected benefits associated with decreased metabolic demands. However, evidence-based guidelines for temperature management in a majority of critically ill patients with fever are still lacking. The aim of our retrospective single-site observational study was to determine the differences in ICU treatment between patients in whom their temperature remained within the target temperature range for ≥25% of time (inTT group) and patients in whom their temperature was outside the target temperature range for <24% of time (outTT group). We enrolled 76 patients undergoing invasive mechanical ventilation for respiratory failure associated with sepsis. We observed no significant differences in survival, mechanical ventilation settings and duration, vasopressor support, renal replacement therapy and other parameters of treatment. Patients in the inTT group were significantly more frequently cooled with the esophageal cooling device, received a significantly lower cumulative dose of acetaminophen and significantly more frequently developed a presence of multidrug-resistant pathogens. In our study, achieving a better temperature control was not associated with any improvement in treatment parameters during ICU stay. A lower prevalence of multidrug-resistant pathogens in patients with higher body temperatures opens a question of a pro-pyrexia approach with an aim to achieve better patient outcomes. Fever has been recognized as a sign of illness for more than 2000 years . It is hFor most patients who are treated in ICUs, fever is associated with infection–pyrogenic fever . TraditiThe aim of our study was to determine the differences in ICU treatment between two groups of patients: patients in whom their temperature remained within the target temperature range for ≥25% of time (inTT group) and patients in whom their temperature was outside the target temperature range for <24% of time (outTT group). Target temperatures and modalities of physical cooling were as per the treating physician.We performed a retrospective observational data collection from January 2015 to December 2019, and informed consent was waived by the institutional ethics committee (No. UKC-MB-KME-61/20). All performed procedures involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. The study was performed in a medical ICU in a tertiary level hospital.n = 76). We excluded patients who were treated with the targeted temperature management for accidental hypo- or hyperthermia, patients after cardiac arrest, neurocritical patients  and patients without a clearly defined target temperature on temperature and therapeutic charts.We included adult (>18 years) mechanically ventilated patients, who were intubated and mechanically ventilated because of respiratory failure associated with sepsis, and in whom physical cooling was used for temperature control (Time outside the target temperature (ToTT) was defined as more than ±0.5 °C outside TT. Temperature deviation of target temperature ±0.5 °C was as per departmental policy and based on physiological basics of human thermoregulation ,11. No p2), maximum level of positive end-expiratory pressure (max. PEEP) and maximum minute ventilation (max. MV) during physical cooling, presence and location of multidrug-resistant pathogens (MDRP) during ICU stay, incidence of sacral pressure sores during ICU stay and incidence of upper gastrointestinal bleeding during ICU stay. The source data for temperature and therapeutic charts was paper-based and electronic for other data. MDRP were, according to the literature, defined as pathogens exhibiting non-susceptibility to at least one agent in three or more antimicrobial categories [We collected basic demographic data and data relevant to ICU treatment, namely outcome of ICU treatment, ICU length of stay, modality of physical cooling, duration of physical cooling, maximum CBT during ICU stay, percentage of time above 39 °C during physical cooling, use of renal replacement therapy during physical cooling, percentage of time outside TT (defined as more than ±0.5 °C of TT), use of acetaminophen, maximum concentration of noradrenaline during physical cooling, maximum fraction of inspired oxygen  and R . For comparison of nominal dichotomous variables, Fisher’s exact test was used. Continuous variables were first assessed for normality using D’Agostino’s omnibus test. Comparison of continuous variables across groups was carried out using Mann–Whitney U-test. Generalized linear models were used in order to additionally estimate the associations adjusted to selected covariates. Bonferroni correction was applied in order to correct for multiple comparisons of survival and hospital course parameters. A statistically significant observation was considered at PWe enrolled 76 adult patients, 49 (64.5%) males and 27 females (35.5%), age 64.4 ± 12.5 years. All patients were mechanically ventilated and required noradrenaline to maintain their blood pressure with a vasoactive inotropic score of 85 ± 36.8. Twenty-seven (35.5%) patients required renal replacement therapy, and admission APACHE II score was 26.1 ± 8.3. Forty-one (53.9%) patients were discharged alive from ICU. General demographic data and parameters describing the course of the treatment in the ICU are described in p = 4.8 × 10−3) where larger dosages were associated with a higher probability for ToTT ≥ 25%. For all other survival and ICU treatment parameters, no statistically significant differences were observed.Time outside the target temperature (ToTT) was defined as more than ±0.5 °C outside TT ,11,13,14We isolated MDRPs in 118 samples obtained from 36 (47.4%) patients. Most were detected in tracheal aspirates  and rectal swabs . The three most encountered MDRPs were from the genus Klebsiella (23.7%), Pseudomonas (22%) and Enterococcus (14.4%) .p = 0.017) (We assessed the associations of MDRP presence in relation to ToTT. We observed that patients with better controlled temperature (less ToTT) had statistically significant higher rates of MDRP presence  as compared to patients with ≥25% of ToTT  .p = 0.021).To additionally confirm the findings, we performed generalized linear models, adjusted as aforementioned, and with the presence of MDRP as a dependent variable . Using rp = 2 × 10−4).Additionally, we assessed if the modality of physical cooling in terms of ECD use exerts an impact on ToTT. Regression models were adjusted to sex, age, renal replacement therapy and TTM duration in days . We obseNeisseria gonorrhoeae infection who were exposed to physical warming in order to achieve a body temperature of 41 °C. This treatment took around 5 h per session, usually requiring 3–4 sessions. Out of 100 patients who undertook the procedure, 64 completed the treatment and 52 were cured of gonococcal disease. Twenty-four patients refused further treatment because they did not like it and 12 could not complete the treatment because of comorbidities [Plasmodium spp., resulting in cyclic increases in body temperature, clearing syphilis in between 50 and 80% of patients, after which malaria was treated with quinine [We observed no major differences in ICU treatment parameters and outcomes between the two groups of patients apart from significantly lower use of acetaminophen, significantly greater probability of the use of esophageal cooling, and significantly higher prevalence of MDRPs in microbiology samples obtained from patients in the inTT group. To the best of our knowledge, there are no clinical studies reporting a higher prevalence of MDRPs in patients with a lower body temperature. There are several potential mechanisms that could couple body temperature with greater efficacy of antibiotics  in the bidities . Similar quinine .2, noradrenaline requirement and duration and ICU survival, implying that a higher level of temperature control and lower mean temperatures during the periods of targeted temperature management are not associated with clinically relevant improvements during the course of ICU treatment. A significantly lower cumulative dose of acetaminophen in the inTT group can be explained by a greater prevalence of more invasive and effective, in our case esophageal, temperature management techniques in the inTT group [In our study, we detected no significant changes between inTT and outTT groups in duration of mechanical ventilation, levels of PEEP and FiOTT group .Similar results have been published by other authors. Young et al. performed a large, randomized, multi-center study comparing a strict temperature control strategy achieved with acetaminophen and with a placebo . They acSome authors suggest there is potential harm in lowering BT in septic patients. Gao et al.  reportedThere are several limitations of our study. First, we performed a small, single-center, retrospective study with inherent biases due to the design of the study. A total of 599 out of 3755 patients that were admitted to ICU during the study period were not included because relevant data (clearly defined target temperatures) could not be extracted from temperature and therapeutic charts. Second, target temperatures were as per the treating physician. However, for this patient population  there are no evidence-based guidelines to set the target temperatures. Third, various techniques and devices were used to manage temperature, such as ice pads, surface cooling with automated systems, esophageal cooling, renal replacement therapy, acetaminophen, different sedatives and opiates that interfere with normal temperature responses, etc. These factors could potentially influence our outcomes [In our study, achieving better temperature control was not associated with any improvement of treatment parameters during the ICU stay. We discovered a lower prevalence of MDRPs in patients with higher body temperatures, which opens a question of a pro-pyrexia approach with an aim to achieve better patient outcomes.