Coprococcus eutactus (family Lachnospiraceae) was presented in hypercholesterolemic subjects. In addition, a positive correlation was established between small RNA ID 2149569 from the species Blautia wexlerae and hypercholesterolemic subjects. Other bacterial and archaeal small RNAs that interacted with the LDL receptor (LDLR) were identified. For these sequences, the prediction of secondary structures was also obtained. There were significant differences in bacterial small RNAs associated with cholesterol metabolism in hypercholesterolemic and normocholesterolemic participants.Cholesterol metabolism is important at the physiological level as well as in several diseases, with small RNA being an element to consider in terms of its epigenetic control. Thus, the aim of this study was to identify differences between bacterial small RNAs present at the gut level in hypercholesterolemic and normocholesterolemic individuals. Twenty stool samples were collected from hypercholesterolemic and normocholesterolemic subjects. RNA extraction and small RNA sequencing were performed, followed by bioinformatics analyses with BrumiR, Bowtie 2, BLASTn, DESeq2, and IntaRNA, after the filtering of the reads with fastp. In addition, the prediction of secondary structures was obtained with RNAfold WebServer. Most of the small RNAs were of bacterial origin and presented a greater number of readings in normocholesterolemic participants. The upregulation of small RNA ID 2909606 associated with  Cholesterol is an essential biomolecule for cells and human physiology . Among iSmall RNAs have been the subject of research in recent years and have been identified in different animals and plants, as well as in many other species and types of cells. Despite this, many of the functions, interactions, and impacts that most of these biomolecules may have on cells are still unknown. Their function is the epigenetic regulation of cellular processes, mainly through the silencing or repression of gene expression . They beIn general, small RNAs participate in cellular homeostasis, regulating vital processes associated with gene expression through messenger RNAs (mRNAs) . Their iPseudomonas aeruginosa, that contained a short RNA that managed to reduce the release of cytokines and the infiltration of neutrophils in cultured human cells and mice, respectively [Helicobacter pylori can reduce the release of interleukin 8 (IL-8) in cell cultures [Although each species presents small RNAs of its own, the interaction between the small RNAs of one species with a different species has also been evidenced, allowing microorganisms, plants, and animals to have a horizontal transfer of information . This waectively . Similarcultures . In a wocultures . AlthougConsidering the above, it has been proposed to use these interactions between microbiota and humans as biomarkers for common and priority diseases, such as cancer and obesity . In addiConsidering the role of small RNAs in the organism and their interaction with various pathologies, this study aimed to identify differences between bacterial small RNAs present at the gut level among hypercholesterolemic and normocholesterolemic individuals.p = 0.525). Similarly, there were no significant differences in SBP, DBP, glucose levels and HDL-C levels. Triglycerides (p = 0.003), total cholesterol (p < 0.0001) and LDL-C (p < 0.0001) were significantly higher in hypercholesterolemic individuals, while weight (p < 0.0001) and BMI (p = 0.004) were significantly higher in normocholesterolemic individuals.Anthropometric, clinical and biochemical parameters are summarized in n = 27) and normocholesterolemic (n = 30) groups, the 10 participants with the highest levels of LDL-C and the 10 participants with the lowest levels of this biomolecule were selected. The selection of the participants with the highest and lowest LDL-C levels was carried out to be able to associate the presence of small RNAs with the most extreme levels of LDL-C in the studied cohort, in order to clearly establish the differences in the expression of small RNAs in individuals with these conditions. It is not ruled out to characterize in the future the small RNAs present in the participants who have intermediate levels of LDL cholesterol to complement the results obtained in this work.The results presented above were originally described by Morales et al. (2022). From the hypercholesterolemic (n = 10) presented an average of 5.32 mmol/L (±0.79), while the normocholesterolemic participants (n = 10) presented 2.00 mmol/L (±0.58) of LDL-C.The selected hypercholesterolemic participants  was completed, bioinformatic analysis was performed. The average raw reads were 12.883.704, while the reads obtained after quality filtering processing were 11,189,004. The sum of all raw reads (n = 20) was 257,674,098. In the case of High-quality reads (n = 20), the sum was 223,780,087. The 86.85% of the total reads presented a Q30 quality score (99.9% base accuracy). When looking at each study group separately, there is a greater dispersion in terms of the number of sequences in hypercholesterolemic participants compared to normocholesterolemic participants. In the latter, a greater number of sequences can be seen on average, both unprocessed in Fastp (Raw) and already processed   .n = 27) and normocholesterolemic (n = 30) groups, the 10 participants with the highest levels of LDL-C and the 10 participants with the lowest levels of this biomolecule were selected. The selection of the participants with the highest and lowest LDL-C levels was carried out to be able to associate the presence of small RNAs with the most extreme levels of LDL-C in the studied cohort, in order to clearly establish the differences in the expression of small RNAs in individuals with these conditions. It is not ruled out that the small RNAs present in the participants who had intermediate levels of LDL cholesterol could be characterized in the future to complement the results obtained in this work.The results presented above were originally described by Morales et al. (2022). From the hypercholesterolemic (n = 10) presented an average of 5.32 mmol/L (±0.79) LCL-C, while the normocholesterolemic participants (n = 10) presented 2.00 mmol/L (±0.58) LDL-C.The selected hypercholesterolemic participants  was completed, bioinformatic analysis was performed. The average raw reads were 12,883,704, while the reads obtained after quality filtering processing were 11,189,004. The sum of all raw reads (n = 20) was 257,674,098. In the case of high-quality reads (n = 20), the sum was 223,780,087. In total, 86.85% of the total reads presented a Q30 quality score (99.9% base accuracy). When looking at each study group separately, there was a greater dispersion in terms of the number of sequences in hypercholesterolemic participants than in normocholesterolemic participants. In the latter, a greater number of sequences could be seen on average, both unprocessed, in Fastp (raw), and already processed   .As a result of principal component analysis (PCA), it was observed that there were differences in the signature of small RNAs in the studied groups. Nine of the ten normocholesterolemic participants were clearly grouped, while the hypercholesterolemic participants presented a greater dispersion. Five of the ten hypercholesterolemic participants were clearly grouped and presented similarity, while four hypercholesterolemic participants were in the normocholesterolemic group. Regarding their LDL-C levels, these were 5.23 mmol/L, 5.46 mmol/L, 5.67 mmol/L, and 5.80 mmol/L, while the participant at the top of the figure had an LDL-C value of 5.34 mmol/L .Among the 20 participants, the total read count assigned as small RNAs was 36,582,653 , with an average of 1,829,133 reads per sample. The number of small RNAs was equivalent to 142,319 in total (7115 on average per sample). When classifying small RNAs, it was obtained as a result that they were associated with bacteria , archaea (1912), and eukaryotes (humans) (5483), and unclassified .In hypercholesterolemic participants, the small RNAs associated with bacteria were 47,560 (4756 on average), the largest number. Those that were unclassified corresponded to 16,508 (1650 on average); those from eukaryotes were 2603 (260 on average); and those from archaea were 905 (90 on average).Regarding normocholesterolemic participants, the small RNAs associated with bacteria were 52,177 (5217 on average) and were the most prevalent, followed by those that were unclassified , those from eukaryotes , and finally, those from archaea . More details are in Coprococcus eutactus , with 89% identity. By performing a recheck analysis with the entire BLAST nucleotide (nt) database, a new hit with 100% identity with the family Lachnospiraceae was obtained. More details are presented in We then obtained the top 10 small RNAs with the lowest binding energy associated with each of the selected mRNA genes related to cholesterol metabolism, and most of them presented between one and two mRNAs as targets, equivalent to 69% of them . HoweverBlautia wexlerae DSM 19850 (p = 0.0013). This corresponds to the most significant result in terms of positive associations; more details can be found in In addition, when performing the correlation between the LDL-C levels and the bacterial small RNAs obtained in our study using the Pearson coefficient, a positive correlation was obtained between the hypercholesterolemic participants and small RNA ID 2149569 associated with the bacterial species When analyzing the binding energy of small RNA 2909606, we found strong complementarity with the mRNA sequences of LIPC, NPC1, LDLR, SREBF1, ABCG1, NPC1L1, RXRA, ABCA1, PCSK9, ABCG8, APOB, CYP7A1, SREBF2, NR1H3, VLDLR, HMGCR, ABCG5, and APOC1. The strongest complementarity occurred with LIPC (−27.26), and the lowest complementarity, with APOC1 (−17.08). Coprococcus eutactus/Lachnospiraceae family), followed by small RNA 676475 (−25.13), associated with Phocaeicola vulgatus. Other small RNAs were associated with the species Bacterioides cellulosilyticus, Candidatus methanomethylophilus alvus, Geobacter sp. SVR, Streptomyces sp. WY228, Aneurinibacillus thermoaerophilus, Clostridium sp. MD294, and Bifidobacterium thermophilum. All the indicated microorganisms are bacteria, except for Candidatus methanemethylophilus alvus, which belongs to the group of archaea. Considering the relevance of the LDL receptor (LDLR) to people’s cholesterol levels, the small RNAs that interacted with the mRNA of this molecule were analyzed. The small RNAs with the best complementarity were obtained; the one that presented the best result among the nine was small RNA 2909606 , 16.34% corresponded to small RNAs. This is because the RNA extraction and purification method applied to the stool samples obtained all these molecules, so in future studies, we will analyze these non-small RNA readings.Considering the role of small RNAs at different levels in the organism, this work aimed to describe the significant differences in these biomolecules associated with cholesterol metabolism. For this, a case-control study was carried out with 10 hypercholesterolemic participants (5.32 mmol/L LDL-C on average) and 10 normocholesterolemic participants (2.00 mmol/L LDL-C on average). Of the total high-quality readings obtained, which were 223,780,087  reported significant differences in terms of the richness, and the Shannon–Weaver and Simpson indices of bacterial communities of gut microbiota between normocholesterolemic and hypercholesterolemic individuals, with these values being notably decreased in the latter group . Our resLachnospiraceae family (89% for C. eutactus), which belongs to the Firmicute group.In both study groups, bacterial small RNA reads predominated compared with archaea, eukaryotic, and unclassified reads. These were also the most highly expressed in general. The above is to be expected, since this group of bacteria abounds in the human gut microbiota, so their biomolecules exist in greater quantity than those of other microbial groups. In addition, many of these molecules play an important role in the physiology and pathophysiology of some diseases. Such is the case of indole substances, intermediary substances that are metabolized by the liver into trimethylaminoxide (TMAO) and short-chain fatty acids (SCFAs), as well as lipopolysaccharides, which induce immunomodulation ,29. It hC. eutactus, the most recent studies associate it with butyrate metabolism in the intestine, where in the presence of D-β-hydroxybutyrate (DBHB), an increase in its abundance in the intestine has been observed [C. eutactus has been associated with the presence of blood in the stools [Focusing on observed . Regardiobserved . On the e stools , when coe stools , Parkinse stools , and irre stools . In addie stools .C. eutactus in hypercholesterolemic participants, a new sequence associated not only with this species, but also with the prediction of interaction with the LDLR. In general, several small RNAs associated with the epigenetic control of the LDLR have been reported, and we highlight, for example, miRNA-140-5P (LDLR of human hepatocytes), miRNA-185 and miRNA-128-1 (which directly affect the LDLR), miRNA-27a and -b (associated with the liver), and miRNA-148a (rodent liver regulation) [B. wexlerae DSM 19850 with this study group (p = 0.0013). B. wexlerae is a bacterium reported to be common in human feces [B. wexlerae, and obesity and type 2 diabetes, showing metabolic improvements and anti-inflammatory effects in murine models [p < 0.0001).Our work associated the regulation of small RNA ID 2909606 with ulation) . In addian feces . Hosomi e models . Other se models . InteresOur results show strong complementarity between small RNA 2909606 and other mRNAs of genes associated with cholesterol metabolism, such as LIPC, NPC1, SREBF1, ABCG1, NPC1L1, RXRA, ABCA1, PCSK9, ABCG8, APOB, CYP7A1, SREBF2, NR1H3, VLDLR, HMGCR, ABCG5, and APOC1. We observed the strongest complementarity with LIPC mRNA (−27.26) and the lowest complementarity with APOC1 (−17.08). Bhattarai et al. (2021), in a review work, established the main small RNAs associated with the epigenetic control of cholesterol metabolism genes, with some of the most important ones being miRNA-33a and miRNA-27a for ABCA1; miRNA-33a and miRNA-128-2 for ABCG1; miRNA-27a and miRNA-128-2 for RXR; miRNA-27a for APOA1, APOB100 and APOE; miRNA-1 and miRNA-206 for SREBF1; miRNA-185 for SREBF2; and miRNA-223 for HMGCS1 ,45.Roseburia sp. and Akkermansia muciniphila were present [Phocaeicola vulgatus), 2473147 (Bacterioides cellulosilyticus), 1622429 , 2119964 (Geobacter sp. SVR), 2634900 (Streptomyces sp. WY228), 1456166 (Aneurinibacillus thermoaerophilus), 233262 (Clostridium sp. MD294), and 3196507 (Bifidobacterium thermophilum). For these small RNAs to exert some kind of effect on human cells in which cholesterol metabolism takes place, it is necessary that they be transported to them. For this, various mechanisms have been postulated with which bacterial small RNAs can finally reach and interact with human cells, with extracellular vesicles (EVs) standing out as one of the most studied [Most of the small RNAs associated with the control of lipid metabolism are of endogenous origin; however, other small RNAs of exogenous origin have also been reported. In a study conducted by Tarallo et al. (2022), it was shown that the profiles of small RNAs are different among omnivorous, vegetarian, and vegan people, and miRNA-425-3p and miRNA-638 were increased when bacteria of the genera  present . At the  present . Our res studied . EVs proBy predicting the secondary structure of these bacterial small RNAs, an initial characterization of the molecules can be obtained to establish their similarities and differences with respect to other small RNAs, both endogenous and exogenous to humans. Our results provide secondary structure predictions for eight of the nine analyzed sequences, as we did not obtain a structure for small RNA 233262.A case-control study was designed with 10 hypercholesterolemic individuals and 10 normocholesterolemic individuals from a total cohort of 57 people who corresponded to the participants described by Morales et al. (2022) .For the analysis of the biochemical parameters, samples of total venous blood without anticoagulant were obtained. For this, the standard venipuncture technique was used. In addition, stool samples were requested of the participants, and the samples were issued and delivered as soon as possible to the laboratory to be frozen at −80 °C. To ensure better viability of the nucleic acids of these samples, DNA/RNA Shield-Fecal Collection Tube  was used.Anthropometric and clinical parameters such as age, height, weight, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were obtained. Biochemical measurements were performed on serum obtained from total venous blood. Glucose, triglycerides, total cholesterol, high-density lipoprotein (HDL) cholesterol (C), and LDL-C were quantified using enzymatic colorimetric methods. Quality measurements were controlled using normal and pathological commercial serums .The obtained data were statistically analyzed using the software Prisma v. 8.0 , with which descriptive statistics were obtained for each variable of the data of all included participants. In addition, the biochemical and anthropometric characteristics of the groups were compared with the Mann–Whitney test.p-values less than 0.05.For all statistical analyses, significance was established at A total of 250 mg of stools from the participants was used for total RNA extraction. For this, a ZR Soil/Fecal RNA MicroPrep™ kit (Zymo Research) was used following the manufacturer’s instructions, which not only allows one to obtain long RNAs but also makes it possible to obtain small RNAs. The RNA obtained was quantified with fluorometry using Quantus Fluorometer™  and analyzed with capillary electrophoresis using Fragment Analyzer™ . For the qualification of samples, RNA had to have an RIN equal to or greater than 7.0. Subsequently, libraries were prepared using a TruSeq Small RNA Library Prep™ kit  following the manufacturer’s instructions. The libraries were quantified with fluorometry using the Quantus Fluorometer™  and analyzed with capillary electrophoresis using Agilent 2100 Bioanalyzer™ . Finally, sequencing was performed with HiSeq 50SE  according to the manufacturer’s instructions.Raw reads were filtered using Fastp v0.23.2  using thThe identification of small RNA candidates was performed using BrumiR , which iTo identify the read counts of each small RNAs for each sample, a mapping procedure was performed using Bowtie 2 . InitialSequence annotation was performed against the genomes of humans, bacteria, and archaea using Basic Local Alignment Search Tool Nucleotide (BLASTn) . The worp-value equal to or lower than 0.01 were considered significantly differentially expressed small RNAs [p-value equal to or less than 0.05 were considered significant.Downstream and differential expression analyses were performed using the R package, DESeq2. The results with a all RNAs . The norFinally, the interaction of differentially expressed small RNAs and the mRNA sequences of twenty molecules considered relevant for cholesterol metabolism in humans  was evalThe prediction of the secondary structure of small RNAs was performed using RNAfold WebServer ,60. The C. eutactus (family Lachnospiraceae), which was upregulated in hypercholesterolemic participants. In this same study group, it was also possible to positively correlate small RNA ID 2149569 from the B. wexlerae bacterium with elevated LDL-C levels. In addition, it is evident that some small RNAs present a strong complementarity with LDLR mRNA, with this being an important receptor associated with cholesterol metabolism in humans. Although there are other molecules involved in cholesterol metabolism, we hope to obtain more results regarding these in the near future. To deepen our findings, more functional analyses are required to validate the results associated with small RNA 2909606 and other small RNAs indicated in this work. The use of cell cultures of hepatoma cells could make it possible to establish the expression levels of LDLR, as well as other cellular molecules of importance in the metabolism of human cholesterol, revealing the direct impact of the described small RNAs. Likewise, we intend to analyze the non-small RNA readings obtained from the sequencing of stool samples to better characterize the different types of RNAs present at the intestinal level in these participants. Our results provide new evidence regarding the prediction of the interaction of RNAs among different species, specifically associating the impact of gut microbiota with cholesterol metabolism, which we hope can be a contribution to understanding the modulation of the metabolism of this molecule with physiological and pathological importance with the control of gut microorganisms and their small RNAs. This becomes relevant when considering the role of cholesterol in cardiovascular diseases and other metabolic diseases and seeking to contribute to a precision medicine approach to be sought in the future in terms of the prevention and treatment of cardiovascular diseases.Our results indicate that there were differences in the expression of small RNAs between participants with hypercholesterolemia and normocholesterolemia, and we highlight small RNA 2909606 associated with


This study assessed a humane endpoint scoring system to detect animal suffering in a rat model of type 2 diabetes. Sprague-Dawley male rats were divided into control and induced (fructose-fed and streptozotocin (STZ) administration) groups. Induced animals drank 10% fructose for 14 days, then received STZ (40 mg/kg) intraperitoneally. Weekly monitoring of body weight, water, and food consumption, 14 parameters of animal welfare, and blood glucose levels were conducted. Results showed weight loss, polyuria, polyphagia, and polydipsia, as well as lack of grooming, narrowing of the orbital area, curved posture, liquid/pasty diarrhea, and abdominal distension. The most useful parameters to evaluate humane endpoints in this type 2 diabetes rat induction model were dehydration, absence of grooming, the posture of the animals, abdominal visualization and palpation, and fecal appearance. The glycemia was significantly higher in the induced group, validating the animal model of diabetes. The humane endpoints table was suitable for monitoring animal welfare.p < 0.01). Induced animals’ murinometric and nutritional parameters were significantly lower than the controls (p < 0.01). Our findings suggest that in this rat model of type 2 diabetes with STZ-induced following fructose consumption, our list of humane endpoints is suitable for monitoring the animals’ welfare.This work aimed to define a humane endpoint scoring system able to objectively identify signs of animal suffering in a rat model of type 2 diabetes. Sprague-Dawley male rats were divided into control and induced group. The induced animals drink a 10% fructose solution for 14 days. Then, received an administration of streptozotocin (40 mg/kg). Animals’ body weight, water and food consumption were recorded weekly. To evaluate animal welfare, a score sheet with 14 parameters was employed. Blood glucose levels were measured at three time points. After seven weeks of initiating the protocol, the rats were euthanized. The induced animals showed weight loss, polyuria, polyphagia, and polydipsia. According to our humane endpoints table, changes in animal welfare became noticeable after the STZ administration. None of the animals hit the critical score limit (four). Data showed that the most effective parameters to assess welfare in this type 2 diabetes rat induction model were dehydration, grooming, posture, abdominal visualization, and stool appearance. The glycemia was significantly higher in the induced group when compared to the controls ( Several animal models have been used to better understand the biopathology of chronic diseases, such as diabetes . The useThe experimental protocol was approved by the University of Trás-os-Montes and Alto Douro Ethics Committee . All the experiments performed on the animals were carried out under the European (European Directive 2010/63/EU) and national legislation (Decree-Law no. 113/2013) on the protection of animals used for scientific purposes.Rattus norvegicus) at three weeks of age and weighing 134.24 ± 10.11 g were obtained from Envigo . The animals were kept in polycarbonate cages with smooth surfaces and rounded edges , three to five animals per cage, with appropriate identification. Animals were negative for viruses, bacteria, mycoplasma, fungi, parasites, and pathological lesions. The bedding for the animals was prepared from corncob , and it was changed every week. Following the introduction of fructose and the administration of STZ, the animals urinate more frequently, demanding daily bedding changes. Polyvinyl chloride tubes were placed in each cage to promote the rat’s environmental enrichment. With these materials, animals can hide and mimic a natural environment. This research was conducted in a controlled environment with a 12-h light/12-h dark cycle at a temperature of 20 ± 2 °C and relative humidity of 50 ± 10%. Fructose, acquired from BioPortugal-Químico, Farmacêutica, Lda , was prepared daily in the drinking water at a concentration of 10% immediately before being provided to the animals to avoid fermenting and placed in opaque bottles. STZ was obtained from BioPortugal-Químico, Farmacêutica, Lda  and prepared before its use in a 0.1 M citrate buffer solution (pH = 4.3).Twenty-four Sprague-Dawley male rats (https://efa.nc3rs.org.uk (accessed on 1 May 2021)). A higher number of animals was used in the group induced due to the higher mortality rate expected in the animals with diabetes when compared with those animals from group control [® Custom Diets, Augy, France). Only the bottles with the 10% (m/v) fructose solution were available to the induced animals during fructose administration  and group induced n = 16). The experimental design was done without “nuisances”, with no need for randomized block designs. After reviewing various published papers concerning animal models of diabetes ,15,16, w. The exp control ,14,15,16stration .Weekly, individual rats’ weight, as well as food and water consumption, were determined, using a top-loading scale .The body weight gain (BWG) for each rat (%) was also determined using the following formula :(1)BWG=BThe following formula was used to calculate the mean daily food consumption for each rat (g) :(2)DailyInitially, water consumption was determined similarly to food consumption. However, with the disease development, the animals from the group induced started to drink more water and, together with the weekly water losses, it became difficult to assess the real water consumption. In this way, the research team decided to calculate the water consumption for just 24 h, with three bottles available per cage. Thus, water consumption from week 4 onwards was calculated by dividing the total cage water consumption by the number of animals in the cage.To monitor the animals’ welfare, the registration of humane endpoints was implemented during the experimental protocol, according to When the animals did not clean themselves, a lack of grooming  was registered. According to Mason et al., chromodacryorrhea can be an indicator of stress and has been described in several studies with laboratory animals . HarderiAdditionally, to the parameters mentioned above, the animals’ body temperature was recorded weekly with a non-contact infrared forehead thermometer . For each animal, the temperature was measured on the forehead, always at the same location, at approximately 3 cm. All readings were performed by the same researcher when the animals stood relaxed in a black-lined cage. To avoid any temperature increase due to stress, all temperature readings were recorded in under one minute.® Areo 2K and blood test strips . The measurements were taken after 12 h of fasting and 2 h after feeding. Blood was taken from the tail by gently “milking” it after a small cut [One week after STZ or vehicle administration  and xylazine , followed by exsanguination by cardiac puncture. Blood samples were collected directly from the heart and centrifuged at 3000 g/10 min for serum separation. Glucose, albumin, triglycerides and cholesterol levels were determined in an autoanalyzer . A complete necropsy was performed in all animals, and the internal organs were macroscopically observed, collected, weighed, and preserved in 10% buffered formalin for 24 h for histopathological analysis. A veterinary pathologist blindly examined 4-µm sections of paraffin-embedded kidneys, stained with hematoxylin and eosin (H&E) under a light microscope.At the end of week 7, the animals were euthanized, after 12 h fasting, by intraperitoneal administration of ketamine  to determine the Lee index and body weight index (BWI) . After tMoreover, the specific rate of weight gain (SRWG) and food efficiency coefficient (FEC) were calculated using the weight of the animals before fasting at the end of the experiment:The food consumption of the animals was measured as the mean per cage, and the mean consumption per animal was considered.® software for Windows . The mean and the standard deviation (SD) were calculated for each parameter. Body weight, food and water consumption, glucose levels during the protocol and temperature were compared using the Ordinary two-way ANOVA with Šidák correction for multiple comparisons. For post-mortem glucose, albumin, cholesterol, and triglycerides levels, kidneys’ relative weight, and murinometric and nutritional parameters, the differences between groups were assessed using Student’s t-distribution. Since the values in p-values lower than 0.05 were considered statistically significant. Boxplots were performed to identify outliers. To avoid bias, data analysis was carried out by both researchers related and unrelated to those conducting the animal experiment.All data were analyzed using GraphPad Prismp < 0.05; weeks 3–7, p < 0.0001). When looking at the body weight gain, some differences were observed between control and induced animals. In fact, a decrease in weight gain over time was observed in both groups, justified by the natural growth of the animals. Throughout the experiment, the weight gain presented several variations in the induced group and was negative in week 3 of the trial (p < 0.0001). However, this was inverted after STZ administration, with the average food consumption in the induced animals higher during the remaining weeks of the experiment (p < 0.0001). Concerning water consumption, induced animals drank more water over time than control animals, and the water intake also increased after STZ injection (p < 0.001).No deaths were recorded during the experimental protocol. The animals’ mean body weight a, after he trial b; that wp < 0.01; Regarding the assessment of animals’ health status by using the parameters stated in the table of humane endpoints, changes were only observed in the induced group . The firp > 0.05). The mean body temperature of animals from both groups ranged from 35.9 to 36.1 °C .Animals from the induced group exhibited higher kidneys relative weight  when compared to controls, as it is shown in To gain a better understanding of the increase in fructose-fed and STZ-injected rat kidneys’ relative weights, a histological analysis of this organ was conducted. As illustrated in When animals presented blood glucose levels higher than the maximum value read by the glucometer (600 mg/dL), the maximum value was attributed to them for the assessment of glycemia in each group. In the first evaluation, 56.25% of the rats exhibited feeding glucose readings higher than 600 mg/dL. This percentage increased to 81.25% in the second measurement (performed in week 6). Three animals from the group induced exhibited blood glucose levels <250 mg/dL 2 h after feeding, which were considered outliers by the statistical analysis performed.p < 0.0001)  .p > 0.05; p < 0.05; Regarding albumin, total cholesterol, and triglycerides levels, no statistically significant differences were found  than in control animals, suggesting that the animals’ normal growth was retarded after exposure. The Lee index, BMI, specific rate of body weight gain, and food efficiency coefficient, factors related to an animal’s body weight and food intake, were also significantly lower in chemically induced animals , respectively), revealing the same tendency as the mean body weight.Except for the initial body weight, the murinometric and nutritional parameters of induced animals were significantly lower when compared with control animals (In general, and following the EU directive, to perform an experimental protocol in European countries is necessary to request authorization from the institutional authorities in which the experimental procedures will be carried out, as a first phase, followed by an authorization request to the national authority responsible for the supervising of the use of animals for scientific purposes. To avoid animal suffering, the analysis of the humane endpoints is required by both bodies. However, the bibliographic references related to the analysis of the humane endpoints in the most diverse research with laboratory animals are scarce and, according to our experience, often difficult to publish. To overcome this problem, our team has collected as much information as possible on this subject over the years to expand the available knowledge. So, envisioning to contribute to this, this research had as a main goal to provide a list of humane endpoints to the rat model of fructose-fed and STZ-induced type 2 diabetes in male Sprague-Dawley rats, as well as to validate this induction model. STZ, together with fructose, is widely used to induce type 2 diabetes in rats .The choice for a 10% fructose solution was based on a previous study that compared the effects of the administration of 10, 20, 30 and 40% fructose solutions on the development of the disease . Once soOver the weeks of fructose ingestion, the induced animals consumed more water, probably due to the sweet taste, and ate less food. These animals began to drink and eat more water and food after receiving STZ. Previous research in both male Wistar and Sprague-Dawley rats found similar results ,26. IndeSome altered parameters in the table of humane endpoints, such as lack of grooming, narrowing of the orbital area, and hunched posture, were indicative of animals’ suffering and stress, most likely because of diabetes development. The Grimace scale described in the table was demonstrated as a scale that, analyzing altered facial expressions in rodents, provides a precise and reliable assessment of the animals’ pain status . ThroughWe strongly believe that this table will be helpful in future experiments on fructose-fed and STZ-induced type 2 diabetes, and depending on the study’s goals, it can be extended to other animal models of diabetes. To better monitor animal welfare, human endpoints can also include the body temperature of animals . DiabeteThe fasting blood glucose levels revealed statistically significant variations at the end of the protocol when the analysis was performed in an automated autoanalyzer, confirming the hyperglycemia associated with diabetes development . FructosOur results suggest that our table of humane endpoints is appropriate to monitor the animals’ welfare in this rat model of type 2 diabetes STZ-induced following fructose ingestion and can be used by other researchers using this model and eventually expanded to other models of diabetes to successfully ensure animals’ welfare. In this study, none of the biological parameters appeared to justify the premature sacrifice of any of the animals; however, if the sum of the assigned scores is equal to or higher than four, the animal must be carefully evaluated and considered its sacrifice.


By fitting the concentration-dependent sensor responses to an adsorption isotherm, we extracted parameters that characterize each analyte-coating combination, including the proportionality and equilibrium constants for adsorption. Principal component analysis confirmed that the sensor array detected and identified mixtures of two TIC gases: NH3/SO2, NH3/EtO, and SO2/EtO. Exposing the sensor array to three TIC mixtures with various EtO/SO2 ratios at a fixed NH3 concentration showed an excellent correlation between the sensor response and the mixture composition. Additionally, we proposed concentration ranges within which the sensor array can effectively detect the gaseous mixtures. Being highly sensitive and capable of analyzing both individual and mixed TICs, our gas sensor array has great potential for monitoring the safety and environmental effects of industrial chemical processes.Toxic industrial chemicals (TICs), when accidentally released into the workplace or environment, often form a gaseous mixture that complicates detection and mitigation measures. However, most of the existing gas sensors are unsuitable for detecting such mixtures. In this study, we demonstrated the detection and identification of gaseous mixtures of TICs using a chemiresistor array of single-walled carbon nanotubes (SWCNTs). The array consists of three SWCNT chemiresistors coated with different molecular/ionic species, achieving a limit of detection (LOD) of 2.2 ppb for ammonia (NH The detection and identification of toxic industrial chemicals (TICs) are essential for workplace safety, public health, and environmental monitoring ,2. Sever2, NH3, toluene, and ethanol using various metal electrodes  . Th. Th632 =2, 30 s) to reduce the signal interference between the sensors. The density of the SWCNT network was estimated by the amount of SWCNTs (16.5 mg/L × 25 μL) and the area of the SWCNT film (1.95 mm2). Then, interdigitated electrodes  were patterned on the SWCNT networks via the lift-off process. Finally, a structure of 70 μm-thick SU8-2050 was patterned around each sensor, which served as a well for drop-drying coating materials.The sensor array was fabricated by patterning electrodes on the SWCNT networks. First, extra nanotubes that were not required were removed by conventional photolithography and oxygen plasma etching  and air cylinders were connected to a mass flow controller  to control the on/off status and flow rate of the gas. The total flow rate was fixed at 500 sccm. To control the analyte gas concentration, the carrier gas was balanced using the MFC. A static mixer was used for the uniform mixing of the analyte gases. The resistance of the sensor was measured using a system switch/multimeter . Prior to gas sensing, the sensor array was stabilized in the chamber using carrier gas for 60 s, and the change in resistance was measured by exposing it to the analyte gas for 20 s. than 1% , small d3, which is lower than the concentration range of NH3 immediately dangerous to life or health (IDLH) (300 ppm)  = 7.63 ppm, and a significant decrease was observed in the increase in resistance when [SO2] increased, until it reached 30.52 ppm (1.06%). The EMIM-coated sensor exhibited a negative response (−0.14%) at [SO2] = 38.2 ppm, and the decrease in resistance increased as [SO2] increased to 61.04 ppm (−2.48%). For the PBS-coated sensor (red), which showed a small increase in resistance upon exposure to NH3 and EtO , which had sensitive responses to NH3 and EtO exposure .To demonstrate the application of our sensor array system for the analysis of complex mixtures, we examined the responses of the sensor array to a three-species mixture of NH and EtO . Because and EtO b,h, a slexposure c,i, the an 1 ppm . As the position b. As the2 = 0.982, 0.986, and 0.941 for the EMIM, PBS, and Ppy-coated sensors, respectively).The relationship can also be described in terms of EtO concentration (RAs the sensor array response showed a linear correlation with the mixture composition, we expect that our sensor array system has potential for the component analysis of the TIC mixtures.3, SO2, and EtO by changing the characteristics of the analyte molecules’ adsorption onto the SWCNT surfaces. Our sensor array not only provided an extremely low limit of detection for a single gaseous analyte, but also allowed the detection and identification of a mixture of gaseous TICs. In addition, the linear correlation of the sensor array responses to component variations in multi-analyte mixtures suggests high potential for the real-time monitoring of TIC gases with qualitative and quantitative component analysis.In summary, we developed a highly sensitive SWCNT-based chemiresistor array for TIC gas sensing. The coating of EMIM, PBS, and Ppy on the SWCNT networks successfully tuned the sensitivity to NH


To explore whether type III prostatitis is related to bacterial infection by detecting the composition and function of microorganisms in expressed prostatic secretion (EPS) of patients with chronic prostatitis (CP) and healthy people.According to the inclusion and exclusion criteria, 57 subjects were included in our study, divided into the healthy group, type II prostatitis group, and type III prostatitis group. 16s rRNA sequencing technique was used to detect and analyze the microbial composition of EPS in each group. Additionally, the metagenomics sequencing technique was used to further explore the function of different bacteria in the type III prostatitis group. Data analysis was performed by bioinformatics software, and the results were statistically significant when P<0.05.Pseudomonas, Haemophilus, Sneathia, Allobaculum, and Enterococcus in CP patients (including type II and III) were significantly different. Still, the relative abundance of different bacteria in type II prostatitis patients was much higher than in type III. The metagenomics sequencing results for the type III prostatitis group showed that the different bacteria had certain biological functions.Many microorganisms exist in EPS in both CP patients and healthy populations. However, the relative abundance of Based on our sequencing results and previous studies, we suggest that type III prostatitis may also be caused by bacterial infection. Furthermore, we aimed to explore whether there is a correlation between type III prostatitis and bacterial infection.22.1According to the inclusion and exclusion criteria, we included 44 patients with CP and 13 healthy participants who underwent fertility examinations at Third Xiangya Hospital of Central South University from June 2022 to December 2022. All subjects were between the ages of 17 and 51. All patients included in the study underwent “two-glass test”  in order to make a definite diagnosis and aid in the classification of prostatitis. According to the classification criteria of NIH, CP patients included in our study were divided into type II and type III prostatitis groups. Healthy people were designated as the control group . The severity of the disease and mental state of the subjects were assessed by using The NIH chronic prostatitis symptom index (NIH-CPSI), Generalized Anxiexy Disorde-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9) scales (All CP patients included in the study met the following inclusion criteria: (i) Were diagnosed with CP according to NIH classification and have the clinical symptoms for more than 3 months ; (ii) diBefore collecting EPS samples, the subjects were asked to empty the urine in the bladder, clean the urethral orifice with sterile saline, and wipe the residual liquid near the urethral orifice with sterile gauze. The same urologist aseptically collected our study’s patient EPS samples. All specimens were stored in a refrigerator at -80°C immediately after collection.2.2® DNA PCR-Free Sample Preparation Kit  was used to construct the library, followed by index coding. The constructed library was quantified using Qubit and evaluated by library quality analysis. Finally, NovaSeq 6000 PE250 was used for sequencing to obtain paired-end reads of 250 bp. Then, the raw data was analyzed  method. The V3+V4 variable regions of the 16S ribosomal RNA (rRNA) gene was amplified by polymerase chain reaction (PCR) using 341F (5’-CCTAYGGGRBGCASCAG-3’) and 806R (5’-GGACTACNNGGGTATCTAAT-3’) primers. The PCR products were purified using 2% agarose gel electrophoresis in 1x TAE buffer, and bands of interest were excised. After equimolar pooling of purified PCR products, the Illumina TruSeq2.3Total genomic DNA was extracted from EPS samples using the CTAB method. The purity and integrity of DNA were analyzed via agarose gel electrophoresis. Nanodrop was used to determine the purity of DNA (OD 260/280 ratio), and Qubit 2.0 was used to quantify DNA concentration accurately. After passing quality control, DNA samples were randomly fragmented using a Covaris ultrasonic disruptor. This was followed by end repair, A-tailing, adapter ligation, purification, and PCR amplification to complete the library preparation process. Following the completion of library construction, Qubit 2.0 was used for preliminary quantification, and the library was diluted and then analyzed for insertion fragment size using the Agilent 2100. Effective library concentration was accurately quantified using Q-PCR. After passing quality control, different libraries were pooled into a flowcell according to the required effective concentration and the target amount of sequencing data. Furthermore, the cBot was used for clustering, followed by Illumina PE150 (2x150) high-throughput sequencing platform for sequencing.2.4https://docs.qiime2.org/2019.1/). The Qiime2 feature-classifier plugin was used to align the representative sequences of ASVs to the pre-trained 13_8 version 99% similarity GREENGENES database (trimmed to the V3V4 region based on the 341F/806R primers) to obtain a table of species classification information  were obtained. According to the unique or common OTUs among the samples, we draw a Venn diagram to visually show the composition similarity and overlap of the samples in each group at the OTU level (3.2.2The species annotation information was obtained by comparing the representative OTU sequences with the Greengene database (Greengenes Database version 13_8). Our study annotated 36 phyla, 100 classes, 165 orders, 237 families, 432 genera, and 274 species. 3.2.3Streptococcus, Lactobacillus, Vibrio, Campylobacter, and Faecalibacterium at the genus level.To investigate the similarities or differences between samples and groups, horizontal clustering was carried out from two aspects of classification information and differences between samples to find the aggregation law of species or samples. The heatmap cluster analysis of all samples was carried out at the phylum, class, order, family, genus  and 11 species in the type III prostatitis group . For details, see Pseudomonadaceae Pseudomonas, Neisseria, Enterococcus, Cupriavidus, Klebsiella, Sneathia, Allobaculum, Haemophilus and Bacteroides) and 7 species in the healthy control group . For details, see Pseudomonadaceae Pseudomonas, Haemophilus, WAL_1855D, Porphyromonas, Sneathia, Allobaculum and Enterocecus) and 4 species in the healthy control group . For details, see The microbes with significant differences between groups were found using the R language DESeq2 package. Multiple comparisons between groups were carried out by the DESeq2 method  . Simultaneously, because the local microecology of patients with CP has been stimulated by low concentrations of antibiotics  for a long time, producing a certain degree of resistance, the clinical efficacy of current drug treatments is not good.When the body is fragile, conditional pathogenic bacteria produce secondary metabolite antibiotics to compete with other normal or beneficial bacteria for living space and resources and finally develop into the dominant bacteria of local microecology, resulting in clinical manifestations of the disease. For CP patients, when conditional pathogens develop into dominant strains and their absolute abundance is higher than the threshold needed for general bacterial culture This study has some noted limitations. First, due to the limitation of testing costs and other factors, the clinical sample size is small, and a larger cohort needs to be evaluated in future studies. Secondly, our conclusions are only based on 16s rRNA, metagenomics sequencing, and related research results. Thus, these findings need to be verified by further basic experiments. Of course, the above deficiencies are also the contents of our follow-up research.5Pseudomonas, Haemophilus, Sneathia, Allobaculum, and Enterococcus are different bacteria in EPS patients with type II and type III prostatitis. Functional analysis shows they may be the main culprits for prostatitis in CP patients. In traditional CP typing, both type II and type III should be related to bacterial infection, but the abundance of pathogenic bacteria in EPS differs. Notably, pathogenic microorganisms’ secondary metabolites may be one reason for the poor clinical drug treatment effect and prolonged course of disease in patients with CP, especially in patients with type III prostatitis.Many pathogenic microorganisms exist in the EPS of CP patients and healthy people. The data presented in the study are deposited in the NCBI repository, accession number PRJNA982408.The studies involving human participants were reviewed and approved by Institutional Review Board (IRB) of The Third Xiangya Hospital of Central South University (Grant No. 22069). The patients/participants provided their written informed consent to participate in this study.W-JS initiated the research question and supervised all aspects of the study; JG and J-WH contributed to data acquisition; YL supervised the statistical procedures of the manuscript; W-JS wrote the first version of the manuscript; ZL and L-YH reviewed this article. All authors contributed to the article and approved the submitted version.


Serotonin syndrome (SS) is a potentially fatal adverse drug reaction characterized by an exaggerated increase in serotonergic activity in the central and peripheral nervous systems. It presents a constellation of signs and symptoms related to behavioral changes, neuromuscular excitability, and autonomic instability. These symptoms can occur in both mild and severe forms. SS can be triggered by the therapeutic use of a drug that increases serotonin (5-HT) availability in the synaptic cleft or by the co-administration of two or more drugs that provide this increase. With the escalating use of antidepressants by the world's population, this adverse reaction may be more recurrent. However, SS is often overlooked by patients or not diagnosed by doctors. This review aims to improve awareness about SS and provide a pharmacological perspective to explain its occurrence. Evidence shows that other neurotransmitters may also be involved with the pathology of SS. Furthermore, SS and neuroleptic malignant syndrome (NMS) seem to be part of the same pathological spectrum, especially in atypical NMS cases. The emergence of the syndrome's symptoms may be closely related to pharmacokinetic and/or pharmacodynamic polymorphisms that lead to an increase in the 5-HT available to or 5-HT signaling by specific receptors, thus constituting an important area for future investigations. Serotonin syndrome (SS), which also can be referred to as serotonin toxicity or serotonin toxidrome, is an adverse drug reaction characterized by an exaggerated increase in serotonergic activity in the central and peripheral nervous systems  ,2. It coDue to the lack of medical awareness about SS, its true incidence is unknown and difficult to measure -6. In 20Another current aggravating factor, which further increases the need for attention and consciousness about SS, is the worsening mental health of the population due to the COVID-19 pandemic. In a cohort study, which used information obtained from the US Veterans Health Administration database, three groups of patients were compared: (1) patients who had COVID-19; (2) patients who did not test positive for the disease during the same period; (3) control group with patients who had influenza in the pre-pandemic period. The results suggest that the first group had a greater risk of developing various mental health disorders such as anxiety, depression, opioid and other substance use, sleep, and stress disorders . In BrazGiven the lack of medical awareness about the syndrome associated with the increased use of antidepressants with several drug interactions that can trigger it, this review aimed to improve the current understanding of SS occurrence using a pharmacological mechanistic approach. Specifically, we analyzed the underlying pharmacological mechanisms that contribute to the development of this syndrome and the drug interactions that can lead to it. Through this approach, we hope to provide new insights to identify and prevent this life-threatening condition.History of serotonin syndromeThe discovery of the antidepressant properties of drugs like iproniazid and imipramine in the 1950s transformed the treatment of depression and anxiety. Iproniazid, initially developed for tuberculosis, was found to inhibit the enzyme monoamine oxidase (MAO), and this inhibition was believed to be responsible for its antidepressant effects . SimilarOates and Sjoerdsma, in 1960, were the first to relate specific clinical manifestations to increased 5-HT levels in the CNS. They observed a syndrome characterized by nervousness, diaphoresis, mental dysfunction, ataxia, and hyperreflexia in patients who were taking an MAO inhibitor (MAOI) while on antihypertensive treatment with L-tryptophan. This syndrome was later named the "Indolamine Syndrome" . In 1975Serotonin system5-HT is a monoamine present in most organic systems, but its best-known action is as a neurotransmitter molecule in the CNS . It is rCurrently, 14 5-HT receptors have already been cloned and are classified into seven families of receptors (5-HT1 to 5-HT7) with different subtypes . Table 1Pathophysiology of serotonin syndromeSix possible mechanisms for the occurrence of SS are listed below and illustrated in Figure Increased Availability of the Essential Amino Acid L-TryptophanTheoretically, an increased supply can lead to an increment in endogenous 5-HT. The study that investigated this risk factor used patients who also used MAOIs, which are drugs that make up the second mechanism for the occurrence of SS . Thus, aInhibition of MAOBy inhibiting MAO, 5-HT is once again stored in the vesicles of the presynaptic neuron and subsequently released into the synaptic cleft to perform its function, which allows for a longer duration of action .Promotion of 5-HT ReleaseSome illicit substances, such as amphetamine, 3,4-methylenedioxymethamphetamine (MDMA), and cocaine can promote the release of 5-HT into the synaptic cleft and thus lead to syndrome onset . It has Postsynaptic ReceptorsThe fourth and fifth mechanisms depend on the interaction of 5-HT and/or agonist or antagonist drugs with their postsynaptic receptors. They can also occur through increased availability of 5-HT in the cleft  ,38.SERT BlockadeAnother way to prolong the action of 5-HT in the synaptic cleft is to inhibit its reuptake into the presynaptic neuron by blocking SERT .5-HT receptors involved in serotonin syndromeThe symptoms that characterize SS can be mainly related to the agonism and/or antagonism of two subtypes of receptors: 5-HT1A and 5-HT2A ,39. TherAnother receptor that appears closely related to the syndrome is the 5-HT2A receptor. The appearance of the most severe SS symptoms may be related to the agonism of these receptors, as its stimulation can cause platelet aggregation, bronchoconstriction, vasoconstriction (and consequent hypertension), and the behavioral and cognitive changes characteristic of the syndrome ,37,44. TOn the other hand, the antagonism of 5-HT2A receptors also seems to be associated with SS onset . A 2018 The two receptors, 5-HT1A and 5-HT2A, are extensively co-expressed in the prefrontal cortex (PFC) ,29,47. WMoreover, the antagonism of 5-HT2A receptors is suggested to be implicated in the increase of serotonergic neurotransmission by inhibiting GABAergic interneurons in the dorsal raphe nucleus . As showA SS animal model, using substances with a strong affinity for 5-HT1A receptors like 8-OH-DPAT and gepirone, was developed utilizing male Sprague-Dawley rats  . After iThe activation of 5-HT3 receptors is related to SS symptoms such as diarrhea, nausea, and abdominal pain. Thus, 5-HT3 receptor antagonists have clinical relevance in relieving nausea and emesis, mainly those resulting from cancer treatment, as mentioned above. Some studies have reported SS cases associated with these antagonists, but the exact mechanism has yet to be elucidated .Occurrences of serotonin syndromeIt is known that SS can occur by an overdose of a drug that increases 5-HT availability or because of a drug interaction between two or more drugs at therapeutic doses . Generally, in SS cases caused by a single drug, the symptoms are moderate, and there is a better prognosis. In contrast, when SS is triggered by drug interactions, mainly by MAOIs and SSRIs, the symptoms are more severe and serious and can lead to death ,52.Other neurotransmitters may also be involved in developing the severe form of SS . For exaDrugs associated with serotonin syndromeAccording to the data found in the literature, the drugs capable of developing SS alone are all SSRIs and nefazodone ,6,57. OnDrugs associated with SS interact with several receptors which go beyond their desired initial interaction. Culbertson et al. (2018) demonstrated additional targets than those described in the technical information leaflets using a bioinformatics approach. They show how these interactions are diverse and with different levels of affinities. For example, naratriptan is a high affinity 5-HT1 receptor subfamily agonist, with a moderate affinity towards 5-HT2A, 5-HT2C, 5-HT3, and 5-HT7 receptors, with no evidence of activity on these receptors being as agonists or antagonists. In addition, this drug has also been shown to have a moderate affinity for SERT, managing to exert a critical inhibitory activity . This stClinical conditions associated with serotonin syndromeThe risk of developing SS appears to be associated with kidney diseases that may predispose the patient due to impaired drug clearance, which circulates longer in the individual's body . Some stHepatic metabolism plays an essential role in developing several drug interactions. Cytochrome P450 (CYP450) is a family of oxidative isoenzymes found in the microsomes of various tissues  responsible for metabolizing several drugs. The drugs involved in SS are mainly metabolized by the isoenzymes CYP2C19, CYP2D6, and CYP3A4 . Some faInterestingly, changes in the pharmacokinetic parameters of SSRIs leading to adverse reactions have been associated with polymorphisms in CYP2D6 and CYP2C19 ,69, mainIn a recent systematic review and meta-analysis carried out with data from 8,379 individuals genotyped for CYP2D6 and CYP2C19, using any of the 14 drugs analyzed (antipsychotics and antidepressants), it was possible to observe that patients with polymorphisms in CYP2D6 had more adverse effects when administered aripiprazole, haloperidol, risperidone, nortriptyline, paroxetine, quetiapine, amitriptyline, mirtazapine, or fluvoxamine. Additionally, patients with CYP2C19 polymorphisms exhibited more effects from escitalopram, sertraline, clozapine, fluoxetine, and venlafaxine  an an37] anHowever, despite the apparent superiority of the Hunter criteria, the gold standard for SS diagnosis, there is no official consensus on which criterion should be used since the algorithm was developed from cases of overdose by a serotonergic agent, and this may make it less appropriate for diagnosing cases in which there is no overdose ,84. The Differential diagnosis Clinical diagnosis of SS can be challenging due to comparison with other syndromes Table  and infeTable Serotonin syndrome versus neuroleptic malignant syndromeNeuroleptic malignant syndrome (NMS) is a significant confounding factor for SS diagnosis ,5,86. ThA vital factor in differentiating the syndromes is the class of drug that initiated the symptoms. While in SS, they would be serotonergic agents, in NMS, they are antipsychotics ,88. HoweSymptoms are also used to guide the differential diagnosis of the two syndromes; for example: in NMS, patients may have stiffness, rhabdomyolysis (characterized by muscle fiber destruction), and increased serum creatine phosphokinase (CPK) levels, which should not occur in SS . HoweverAnother critical point for differentiating the syndromes is the speed with which the symptoms appear after introducing the new drug to the therapy. Many authors support that SS symptoms appear in less than 24 hours and that in NMS, symptoms present between 24 and 72 hours ,86. HoweIt is known that MAOIs have a very potent effect due to their mechanism of action that inhibits the main pathway of 5-HT metabolism. Thus, some authors argue that the interaction between an MAOI and an SSRI is more likely to cause more severe and even fatal cases of SS ,97. Fox Therefore, this delay in the appearance of SS symptoms may be due to the change in the pharmacological profile of drugs currently prescribed. This result may modify that well-established temporal parameter of the fastest initiation of SS symptoms. In addition, Werneke et al. (2016) demonstrated that the dose and speed of drug distribution might cause a more rapid or slower syndrome onset .Given all these similarities and difficulties in differentiating these two syndromes, some authors consider them part of the same pathological spectrum ,99. InteTreatment and preventionThe first and foremost treatment of the syndrome is removing the causative serotonergic agent. The symptoms of SS disappear spontaneously in 70% of the cases, with only the withdrawal of the drug(s) involved. The level of additional support will be given depending on the severity of the patient's symptoms; in general, 40% of patients end up needing intensive care, with 25% requiring drastic measures such as orotracheal intubation ,84.In addition to withdrawal from serotonergic agents, some mild cases also require benzodiazepines to improve akathisia and anxiety. Benzodiazepines can also be used in cases where the patient has myoclonus, muscle rigidity, and seizures. Beta-blockers  also block 5-HT1A receptors, which would be beneficial in moderate cases with hypertension ,84. HoweIn the most severe SS cases, often considered a medical emergency, some authors advocate administering cyproheptadine (a non-selective 5-HT1A and 5-HT2A receptor antagonist) if benzodiazepines fail to control symptoms ,104. ThiIn patients with elevated body temperature caused by SS, cooling measures and reducing muscular activity is an effective treatment strategy ,104. ThiWhile most cases resolve spontaneously, there are commercially available extended-release formulations releasing the drug associated with SS slowly over time, which may change this estimate. In addition, it is always necessary to consider these drugs' half-life and whether their metabolites have pharmacological effects .The main form of SS prevention is knowledge of its possible causative agents and symptoms by the entire care team, physicians, and patients ,4,52. InSS is commonly associated with patients receiving treatment for psychiatric disorders. However, since antidepressants are also used to treat other medical conditions, all physicians (not only psychiatrists and neurologists) who prescribe drugs that can induce SS should be educated about this syndrome. Moreover, providing education to patients who are starting antidepressant treatment about the potential adverse reactions and the risk of developing SS is crucial.The appearance of the syndrome's symptoms in some patients, despite others with very similar conditions, leads us to consider that polymorphisms are the risk factors favoring the increase of 5-HT available in the synaptic cleft. Future studies investigating polymorphisms in critical molecules for the kinetics and/or targets  of SS-inducing drugs will yield a greater understanding of the syndrome.


Twin pregnancies demonstrate a 2–3-fold higher chance of developing PE compared to singletons, and recent evidence has demonstrated that the sFLT1/PIGF ratio is strongly associated with PE, adverse pregnancy outcomes, as well as imminent deliveries due to PE complications. The primary objective of this systematic review was to summarise the available data on the levels of sFLT1, PlGF and their ratios in twin pregnancies and to investigate their association with the development of PE, adverse pregnancy outcomes and the timing of the delivery. A systematic search of Ovid Embase, Web of Science, Science Direct, PubMed, Ovid Medline, Google Scholar and CINAHL was carried out. sFLT1 levels and the sFLT1/PIGF ratio appeared higher in twins compared to singleton pregnancies, especially in the third trimester, while PlGF levels appeared higher up until the third trimester, with their values showing no difference or being even lower than in singletons thereafter. The sFLT1/PIGF ratio has been reported to be an independent marker of adverse outcomes related to pre-eclampsia and is associated with the mean time until delivery in an inverse manner. Further research is required in order to establish the optimal sFLT1/PIGF cut-off values and to stratify the risk of adverse outcomes in twin pregnancies. Pre-eclampsia (PE) is a multiorgan pregnancy-specific disorder with an overall incidence of 5–8% among all pregnancies and is one of the main causes of maternal morbidity and mortality globally ,2. Twin PE has been associated with an underlying dysregulation of angiogenic factors, such as soluble fms-like tyrosine kinase-1 (sFLT1) and placental growth factor (PlGF), and recent evidence has underlined those findings by demonstrating that the sFLT1/PIGF ratio is strongly associated with PE, adverse pregnancy outcomes, as well as imminent deliveries due to PE complications in singleton pregnancies ,7,8. TheHowever, the current evidence regarding twin pregnancies is quite scarce and conflicting so far. The present study will aim to review the available published data that focus on the alterations of sFLT1, PlGF and their ratio in twin pregnancies compared to singleton pregnancies and/or their association with the time until delivery and the development of PE and adverse pregnancy outcomes.The literature search was carried out by the two reviewers, I.S. and A.R., across seven databases: PubMed, OVID MEDLINE, Google Scholar, OVID EMBASE, Web of Science, Science Direct and CENTRAL  between 19 November and 20 November 2022. The PRISMA guidelines were followed for the drafting of this paper. Each reviewer used the same search strategy comprising all the necessary keywords  and a period spanning from January 2010 to September 2022 in all databases. CENTRAL was searched by both reviewers. The abstracts of the citations were examined by I.S. and A.R. to identify all potentially relevant articles, which were then examined in full-text form. Reference lists of relevant original and review articles were hand-searched for additional reports. Email alerts were put in place to notify the reviewers of new results after the search period. The two reviewers screened the search results independently, and discussions were held at both screening stages alongside a third author (V.P.) to resolve any disagreements before the final papers were decided.Selection criteria were based on the predetermined characteristics, starting with the population in question being pregnant women of any age with twin pregnancies in which the angiogenic factors in question were examined. It should be noted that we included all the available research that assessed sFLT1, PlGF and their ratio, irrespective of the gestational age of the laboratory assessment. Furthermore, we ended up including only the projects that assessed, first, the levels and values of those factors in twin pregnancies and, second, their potential association with pre-eclampsia and composite adverse pregnancy outcomes .The search identified 199 potentially relevant studies, however, 186 were excluded because they were non-relevant articles . In total, only 13 peer-reviewed papers were considered to be relevant, and their data was extracted ,21,22,23Given the heterogenicity of the included studies in terms of the populations that were examined , the diversity of the outcome measures  and the different gestational periods of investigation for the biomarkers under question, a meta-analysis was deferred.The methodological qualities of the included studies were assessed by two independent reviewers using the QUADAS-2  tool that evaluates the technique of patient selection, the indexed test, the reference standard to that test and the flow and timing of the test/study. When the two authors disagreed, a final consensus was given by a third reviewer (V.P.) . The oveFaupel-Badger et al. assessed the changes in sFLT1, PlGF and the sFLT1/PlGF ratio in multiple pregnancies compared to singletons longitudinally through pregnancy . The res+6 w, 15 to 19+6 w, 20 to 23+6 w, 24 to 28+6 w, 29 to 33+6 w, 34 to 36+6 w, 37 w to delivery), and it was shown that, while the median sFLT1/PlGF ratio demonstrated no difference between multiple and singleton pregnancies up to 29 weeks of gestation, its levels appeared higher thereafter. sFLT1 values appeared higher through every gestational age window, with the rise being more prominent after 29 weeks of gestation, a fact that is in accordance with the increased sFLT1/PlGF ratio levels during the third trimester. In terms of the PlGF levels, their concentrations appeared higher in general except in early pregnancy (10 to 14+6 weeks) and between 29 to 37 weeks, with a sharp increase beyond that gestational age (37 weeks to delivery).Similar results were extracted by a recent study , which i+0 to 13+6 weeks of gestation) and one in the third trimester (32+0 to 34+0 weeks of gestation). It was demonstrated that, in the first trimester, sFLT1 was significantly higher in DC compared to MC, while PlGF and the sFLT1/PlGF ratio showed no statistically significant difference among the two groups. sFLT1 remained higher in DC compared to MC twin pregnancies in the third trimester, PlGF was still no different among the two groups, however, the sFLT1/PlGF ratio appeared significantly higher in the DC group due to the higher rise in sFLT1. Finally, in accordance with the previously published studies, it was shown that, in both DC and MC pregnancies, the angiogenic factors under examination showed an increase in the third trimester compared to the first, with the PlGF rise being the most prominent of alterations that led, as expected, to lower levels of the sFLT1/PlGF ratio.Recently, Kozlowski et al. published the first study to associate the concentrations of the biomarkers under question with chorionicity.  It was ap = 0.03), with the correlation being stronger for gestational periods of less than 34 weeks .Rana et al.  was the p < 0.001, within 2-weeks median: 84.2 versus 23.5; p < 0.001), while the levels of PlGF were significantly lower in both study samples compared to women that remained pregnant after 2 weeks. They concluded that a cut-off of 38 could rule out delivery within 1 and 2 weeks with a negative predictive value of 98.8  and 96.4% , respectively.In their twin study, Binder et al.  investigp = 0.010), a finding that could not be reproduced in late-onset PE cases.Similarly, Karger et al.  used thep = 0.0004), PlGF was lower (162.5 pg/ ml (98.0–226.5) versus 224.0 pg/mL (156.0–449.0), p = 0.005) and the sFLT11/PlGF ratio was elevated (74.2 (43.5–110.5) versus 36.2 (7.1–71.3), p = 0.0005). The difference in the sFLT1/PlGF ratio was more pronounced when patients presented <34 weeks (97.7 (76.6–178.1) versus 31.7 (6.5–48.7); p = 0.001).The first study in this review to evaluate the role of angiogenic factors in women with twin pregnancies and suspected pre-eclampsia was conducted by Rana et al. . In thisp ≤ 0.001 and p = 0.037, respectively)).Similar results were reproduced by Droge et al. . In theiShinohara et al.  measuredp = 0.002) and foetal growth restriction (FGR) ; p < 0.001). The mean sFLT1/PlGF ratio, though, was not significantly different amongst the two groups .Similarly, Martínez-Varea et al.  studied p < 0.001) were increased and values of the ratio  were decreased in twin pregnancies with pre-eclampsia.On the contrary, Saleh et al.  evaluatep = 0.075). They also conducted an ROC analysis that revealed no predictive value for sFLT1/PIGF and composite perinatal adverse outcome , however, it demonstrated a significant association with sFLT1/PIGF and s-FGR .More recently, Karge et al.  did not Finally, expressed as adjusted multiples of the median, Boucoiran et al.  investigThe present work aims to summarise the currently published data on the alterations in the levels of the angiogenic factors sFLT1 and PlGF and their ratio during pregnancy, as well as their association with adverse pregnancy outcomes. A summary of the observations and main conclusions of the included studies is depicted in According to the available evidence, sFLT1 levels appeared higher in twin pregnancies throughout the pregnancy, generating a massive rise, especially after 29 weeks of gestation, while PlGF levels began by being slightly higher only to decrease in no different or even lower levels compared to singletons in the third trimester. ,22 ContrIn terms of the effect of the underlying chorionicity on the above-mentioned factors, Binder et al.  and CallAs stated earlier, an elevated sFLT1/PlGF ratio is associated with shorter MTUD. Several studies have assessed such a correlation in twin pregnancies, reproducing similar outcomes as in singletons. Rana et al.  demonstrThe role of angiogenic biomarkers in singleton pregnancies with PE has been well established . Current+0–30+6 weeks of gestation could be a useful predictor for pre-eclampsia in twin pregnancies within 4 weeks from blood sampling. In line with the above findings, however, in earlier gestation, Martínez-Varea et al. [Several studies have suggested different levels of angiogenic markers in singleton and twin pregnancies, highlighting that those cut-offs used in singleton pregnancies for the prediction of PE and/or adverse outcomes related to PE may not be applicable in twin pregnancies. Saleh et al.  and Droga et al.  observeda et al. ,32.Although PE and FGR are different entities, they share common pathogenic pathways that include inadequate placentation, inflammation, and maternal vascular dysfunction . Two stuIt is noteworthy that most of these studies are observational studies, and the actual effect of the sFLT1/PlGF ratio assessment when managing twin pregnancies and suspected pre-eclampsia remains unknown.To our knowledge, this is the very first systematic review to include all the available published data regarding the association of the angiogenic factors sFLT1, PlGF and the sFLT1/PlGF ratio in twin pregnancies with the subsequent development of PE/HELLP, FGR and the timing of delivery. This review also discusses the alterations of those biomarkers in twins compared to singleton pregnancies.The main limitation of this review can be summarized as the small sample size of the included studies. To be more precise, eight of the included studies analysed data from less than 100 twin pregnancies, which was emphasised by their respective authors. Furthermore, the methodological study design and the sample population of the included studies were quite heterogenous, given the fact that they were both prospective and retrospective cohort studies as well as case-control studies, while in terms of the populations under examination, four studies mixed singleton and twin populations, and seven only included twin pregnancies. Last but not least, the investigation of the biomarkers under question took place in several gestational periods, ranging from 9 up to 39 weeks of gestation, and, as such, the extraction of any conclusions in terms of the prediction or management of adverse pregnancy outcomes should be guarded. As stated earlier, the above-stated heterogeneities and the diversity of the outcome measures under investigation among the included studies were the reasons that led us to defer conducting a meta-analysis since we concluded that the results would be quite questionable and possibly confusing.The alterations of the levels of the angiogenic factors sFLT1, PlGF and their ratio in twin pregnancies are parameters that demonstrate a potential additive value for the prediction of the timing of delivery and the development of PE and foetal growth restriction. Given the low prevalence of twin pregnancies, future research should focus on the design of multicentre studies that would include an adequate sample size of twin pregnancies, which will facilitate concrete results to be extracted. Similar to singleton pregnancies, the aforementioned biomarkers could be implemented in screening and management policies, however, for such strategies to be achieved, the evaluation of those factors needs to be performed in several gestational periods, in different types of twin pregnancies (monochorionic and dichorionic), for the determination of an optimal cut-off and for their potential benefit in terms of perinatal and neonatal outcomes.


Our results show that serum is more concentrated and less variable for most of the shared metabolites than the three saliva types. While we found moderate to strong correlations between serum and saliva concentrations of specific metabolites, saliva is not simply an ultrafiltrate of blood. The intense oral metabolism prevents very strong correlations between serum and salivary concentrations. This study contributes to a better understanding of salivary metabolic composition, which is crucial for utilizing saliva in laboratory diagnostics.Saliva, which contains molecular information that may reflect an individual’s health status, has become a valuable tool for discovering biomarkers of oral and general diseases. Due to the high vascularization of the salivary glands, there is a molecular exchange between blood and saliva. However, the composition of saliva is complex and influenced by multiple factors. This study aimed to investigate the possible relationships between the salivary and serum metabolomes to gain a comprehensive view of the metabolic phenotype under physiological conditions. Using  Whole saliva (WS) contains molecular information that may reflect the health status of an individual. It is a complex water mixture composed of organic and inorganic secretions of major and minor salivary glands, gingival crevicular fluid, suspended bacteria, desquamated cells, and food debris ,2,3. ItsThe major glands that bilaterally secrete saliva are the parotid, submandibular, and sublingual glands A. Under Salivary glands are composed of specialized epithelial cells that form acini, the glandular end portions that initiate the secretion of saliva A, and brA dense network of capillaries surrounds the ductal system, while a less intense vascularization supplies the terminal acini. When stimulated by parasympathetic vasodilator fibers, blood flow increases up to 20-fold and produces the flux of a solution, containing inorganic and small organic substances B, towardOver the last two decades, knowledge about saliva and its homeostasis has increased to such an extent that a new term, salivaomics, has been coined to describe the reservoir of information present in WS, referring to the subject’s genome, epigenome, transcriptome, proteome, metabolome, and microbiome ,15. Among the organic macro-components, salivary proteins have been extensively investigated, revealing their glandular  and plasma  origin . The pre1H-NMR spectroscopy and (b) a comparison of their serum and salivary metabolite profiles. The comprehensive analysis of metabolites in a salivary specimen has been employed (a) as a source of biomarkers for oral and general disorders, (b) to study changes in saliva composition with exercise performance, and (c) to correlate the salivary metabolome with oral microbiome ,19,20,21Serum, parotid saliva (PS), submandibular/sublingual saliva (SM/SLS), and WS samples were collected from each subject according to a previously established protocol . 1H-NMR spectroscopy allowed the identification of 43 metabolites in the serum of all study participants. Metabolomic analysis based on Each participant released the three salivary samples (5.4 mL) at different rates . The meaA list of the metabolites’ mean concentrations in the three types of saliva and an analysis of the resulting salivary profiles have already been published ,23. The The number of metabolites detected in all sample types was computed and plotted in a Venn diagram . In thisn = 27). The PLS-DA score plot presented in We performed a partial least-squares–discriminant analysis (PLS-DA) using the NMR-derived concentrations of the metabolites shared by all sample matrices  were Urea, Glutamine, Alanine, and Taurine. Serum exhibited higher levels of Glutamine and Alanine than the salivary samples. The role of these amino acids as gluconeogenic precursors, being mainly released by muscle tissue during fasting, may explain these findings. However, Taurine’s prevalence in PS suggests a role of this metabolite in salivary gland function.We used the Mann–Whitney test to compare the metabolite composition of serum and PS or SM/SLS. The concentration distribution significantly differed for all metabolites, except for Choline in serum and PS and Creatine, Glutamate, and Urea in serum and SM/SLS. We performed a correlation analysis for metabolite concentrations. For most of them, the correlation was negligible when comparing serum to PS or SM/SLS. Based on Pearson analysis, the highest degree of correlation was obtained with 2-Hydroxyisovalerate, 2-Hydroxybutyrate, and 3-Hydroxybutirate (0.5 ≤ r < 0.7) when comparing serum to PS and with Phenylalanine (r = 0.5) and Pyruvate (r = −0.5) when comparing serum to SM/SLS. In serum vs. PS, Spearman correlation analysis confirmed all the strong/moderate correlations found in Pearson analysis and assigned a moderate correlation (|r| ≈ 0.5) to Creatine and Lactate. In serum vs. SM/SLS, Spearman correlation analysis confirmed the moderate negative correlation of Pyruvate found in Pearson analysis and assigned a moderately positive correlation (r ≈ 0.5) to 3-Hydroxybutyrate, Creatine, and Creatinine. We calculated the concentration ratios of “serum/saliva” for each metabolite present in serum and saliva. These ratios were averaged across the 20 study participants and are referred to as “mean fold change”, a parameter that expresses the metabolite concentration difference between serum and saliva samples. In the comparison of serum vs. PS A, among Metabolites with a mean fold change between 0.5 and 3 are listed in With a mean fold change <0.5 threshold, Acetate, Glutamate, and Creatine were significantly more concentrated in PS than in serum, while only Acetate was significantly more concentrated in SM/SLS than in serum A,B. Finally, the data indicate that the metabolites shared by the two major salivary gland samples were more concentrated in PS than SM/SLS .Despite the complex origin of the WS, metabolite levels were compared using the same approach as for PS and SM/SLS. Statistical analysis showed that serum and WS concentrations differed significantly except for Choline and Tyrosine.Based on Pearson analysis, 3-Hydroxybutyrate, Creatinine, Glutamate, and Lysine concentrations correlated with the corresponding serum concentrations with a coefficient >0.5 . Spearman correlation analysis confirmed the strong correlation of 3-Hydroxybutyrate and Creatinine found in Pearson analysis and assigned a moderate correlation (│r │≈ 0.5) to Lactate and Lysine.Most of the mean fold changes (74%) fell between 1 and 20, with a few close to 1 . Among tAcetate and Glutamate were present in WS at a higher concentration than in serum for all study subjects. This is consistent with the result obtained when comparing serum and PS and partially in line with the comparison between serum and SM/SLS , where tRecent metabolomics studies have been performed to compare the diagnostic performance of serum and saliva or investigate their relationships in various pathologies ,26,27,281H-NMR-based metabolomics, and generated the PS, SM/SLS, and WS metabolite profiles of young and healthy subjects [We have already devised a standardized procedure for saliva collection, optimized the preparation of samples for subjects ,23. Intehttps://hmdb.ca, accessed on 30 September 2023) [1H-NMR Urea signal may have been affected by the exchange with water, leading to an underestimation of Urea serum concentration. The mean metabolite concentrations obtained from the sera of the cohort of young and healthy subjects of this study were coherent with the data reported in The Human Metabolome Database  , except n = 20), serum samples revealed a more compact distribution when compared with WS and PS samples [Based on the total signal area distribution of the NMR spectra  to 3-Hydroxybutyrate, in line with the serum vs. PS correlation analysis results. As this metabolite is more concentrated in serum than saliva, the parotid and submandibular/sublingual glands likely use the same molecular mechanism to transfer it from blood to saliva. Based on Pearson and Spearman analyses, 3-Hydroxybutyrate, Glutamate, Creatinine, and Lysine showed a positive correlation when comparing serum vs. WS. As for 3-Hydroxybutyrate, our data agree with the study of Miyazaki et al. . We founOverall, for most of the metabolites shared by serum and saliva, we observed a prevalence of serum concentrations over the salivary ones  and 15 µL of 1% 3-trimethylsilyl propanoic acid (TSP) in 2.5% D2O .For NMR sample preparation, sera were thawed at room temperature and, since the presence of plasma proteins may interfere with metabolite quantification via NMR , they we1H-NMR spectra of serum samples were acquired at 25 °C with a JEOL 600 MHz ECZ600R spectrometer  as described in [One-dimensional ribed in . The spehttps://www.metaboanalyst.ca, accessed on 30 September 2023) [Multivariate statistical analysis was conducted on target metabolites using Metaboanalyst 5.0 (er 2023) . Partialer 2023) . VIP scon = 20).Data were analyzed using Origin 2019 software. Descriptive statistics for each sample type and variable are presented as mean  ±  SD or median and interquartile range. The mean fold change was obtained for each metabolite by averaging the ratios calculated by dividing the serum concentration by the PS, SM/SLS, or WS concentration of all study subjects  ranges from +1 (perfect positive correlation) to –1 (perfect negative correlation). For absolute values of r, the correlation is considered strong if 0.6 < r < 0.8, moderate if 0.4 < r < 0.6, and weak if 0.2 < r < 0.4. p < 0.05.For all analyses, the significance level was set at Metabolites with concentrations below the quantification limit  in ≥20% Serum and salivary metabolite profiles derived from samples collected concurrently from healthy subjects under physiological conditions were compared to assess the relationships between their compositions. Many researchers have focused exclusively on WS, especially when searching for biomarkers, because it can be collected using non-invasive procedures and is rich in many analytes of interest for diagnosis or screening. Instead, we intended to study the three types of saliva separately to provide a comprehensive analysis of their metabolomes in relation to serum composition under physiological conditions. Our results highlight that none of the saliva types are a mere ultrafiltrate of blood, as saliva composition is influenced by several factors, such as intrinsic glandular metabolism and the enzymatic activities of the oral microbiota. The proximity of the capillary bed and acinar/ductal structures suggests that metabolites may diffuse or be transported from serum to saliva because of a favorable concentration gradient and/or via specific transport systems. Based on our analyses, however, an intense oral metabolism is likely to alter the concentrations of most excreted metabolites, both at the gland level and in other oral niches, thus preventing very strong correlations between serum and salivary concentrations. In some cases, the salivary metabolite was not detected in the serum, or its concentration exceeded the serum level, indicating intense endogenous production. We did find some moderate to strong correlations between the serum and saliva concentrations of specific metabolites, supporting the idea that these metabolites may have been transferred from serum to saliva to an extent dependent on their concentrations. Our results align with the literature focused on those metabolites. In terms of future applications, the metabolomic profiles of serum, WS, PS, and SM/SLS generated using standard operating procedures and the resulting assessments may contribute to a valuable knowledge base for salivary metabolomics aimed at identifying biological markers of oral and systemic health disorders ,30.