Thyroid disorders (TD) and diabetes mellitus (DM) are the two endocrinopathies with the highest prevalence in the general population that frequently coexist. Thyroid dysfunction is more common in people with type 2 diabetes mellitus (T2DM) compared to normoglycemic individuals. Untreated TD can impair glycemic control, increasing the risk of diabetes complications. Hyperinsulinemia can affect the morphology of the thyroid gland by promoting the proliferation of thyroid tissue and increasing the size of thyroid nodules. Metformin can confer benefits in both endocrinopathies, while other antidiabetics, such as sulfonylureas, can negatively affect thyroid function. Animal and human observational data suggest an increased risk of medullary thyroid carcinoma after treatment with glucagon-like peptide-1 receptor agonists. However, randomized trials have so far been reassuring. Furthermore, some observational studies suggest an association between thyroid cancer and T2DM, especially in women. This narrative review aims to shed light on the epidemiological, pathophysiological, and clinical aspects of the interplay between TD and T2DM. Taking into account the important clinical implications of the coexistence of T2DM and TD, proper screening and management strategies are needed for both endocrinopathies to ensure optimal patient care. Diabetes mellitus (DM) is the most common endocrinopathy worldwide and is characterized by insulin resistance and/or deficiency, leading to hyperglycemia. According to the International Diabetes Federation, in 2021, the global prevalence of DM in the adult population was approximately 10.5% 537 million), and this number is expected to increase to 643 million by 2030 [7 millionThe coexistence of DM and TD is frequently observed in daily practice. A higher prevalence of TD has been documented in people with DM compared to normoglycemic individuals, while patients with both endocrinopathies have poorer glycemic control and are more vulnerable to the development of complications . There aThe prevalence of TD is significantly higher among patients with T2DM compared to the general population, ranging from 9.9 to 48% in various studies . A studyAnother study in Saudi Arabia revealed that the prevalence of TD was 16% in patients with T2DM. This study included 100 individuals with T2DM and 100 age- and sex-matched normoglycemic controls. Thyroid autoimmunity  was detected in 10% of individuals with T2DM versus 5% of controls. Additionally, antibodies to glutamic acid decarboxylase 65 (GAD65ab) were found in 26% of patients with T2DM and 2% of controls. In GAD65ab-positive patients with T2DM, thyroid autoimmunity was observed in 27% versus 4% in GAD65ab-negative patients, and TD was diagnosed in 42% versus 7%, respectively . A GreekCheng et al.  conducteThyroid hormones (THs) affect the regulation of glucose homeostasis and lipid metabolism through both the central nervous system and directly in peripheral target organs such as the liver, skeletal muscle, pancreatic beta cells, and white and brown adipose tissues . In partA large-scale cross-sectional study by Gu et al.  demonstrTSH stimulates deiodinase expression and activity ,23. ElevInsulin resistance and excess insulin release observed in DM induce the proliferation of the thyroid gland, thus increasing the incidence, size, and volume of thyroid nodules ,27. FurtDM interferes with thyroid function by modifying TSH levels and inhibiting the conversion of T4 to T3 in peripheral tissues ,29. In iVisceral adiposity is positively correlated with insulin resistance and the risk of T2DM . TSH levTH excess leads to several alterations in peripheral organ targets and induces hyperglycemia and insulin resistance. THs exert a key role in hepatic glucose metabolism, with stimulative effects on liver glucose production and insulin requirement. They stimulate the hepatic expression of the glucose transporter GLUT2, resulting in increased hepatic glucose output . Also, iUnlike the liver, THs act synergistically with insulin in the peripheral tissues. T3 upregulates the expression of GLUT4, adenosine monophosphate-activated protein kinase, and acetyl coenzyme A carboxylase, which are involved in basal and insulin-stimulated glucose transport, utilization, and glycolysis in skeletal muscle ,46. T3 u+ and L-type Ca2+ channels. Abnormal glucose tolerance is not caused by insulin resistance but rather by a defective pancreatic beta cell response to glucose [In the normal range, T3 influences insulin production by pancreatic beta cells since neonatal beta cells have TH receptors, and their exposure to T3 promotes the activation of the transcription factor MAFA, which stimulates beta cell maturation. However, in hyperthyroid rats, the pancreas is seen to increase, but both the islets’ capacity and the overall number of insulin-positive cells are decreased. A significant decline in islet function and decreased glucose-induced insulin production appear to be caused by a decrease in beta cell mass and dysfunction of the insulin secretory pathway, which involves two essential components: ATP-sensitive K glucose ,50,51. F glucose .Patients with hypothyroidism may also develop insulin resistance, although there are some crucial differences compared to those with hyperthyroidism. Clearly, low levels of THs affect several organs. First, there is reduced glucose absorption by the gastrointestinal tract. Decreased hepatic glucose output is observed due to diminished liver and muscle gluconeogenesis and glycogenolysis . RegardiRegarding beta cell function, glucose-induced insulin secretion is increased, and several studies have shown that insulin levels are elevated in hypothyroidism . FurtherThe full spectrum of risk factors for TC is not well established. Environmental exposure to neck irradiation, inadequate iodine intake, family history of TC, and lifestyle factors are believed to be associated with the increase in prevalence ,63,64,652, but this correlation was stronger in men [The worldwide origin of the studies provided considerable population heterogeneity and indicated that the risk associated with DM was more prominent among women in areas with a high incidence of TC compared to other geographical areas . Anotherr in men . On the r in men  and a por in men  did not r in men .There are some suggestive molecular biological mechanisms that could explain the association between DM and the increase in the incidence of TC. First, hyperinsulinemia observed in patients with T2DM can impede cell apoptosis and promote cell proliferation through stimulation of insulin and the insulin-like growth factor-1 (IGF-1) pathway . IncreasSecond, long-term exposure to higher TSH levels was correlated with an increased probability of DTC and a more aggressive tumor stage . ElevateGlucose can increase the production of reactive oxygen species, especially nitric oxide . VitaminAdiponectin acts as an insulin-sensitizing, anti-inflammatory, and anti-tumor agent, the latter by inhibiting cell proliferation and angiogenesis and increasing apoptosis. Low adiponectin levels observed in central obesity aggravate and are aggravated by insulin resistance, resulting in a vicious cycle with metabolic, inflammatory, and possibly oncogenic consequences ,82. MitsMultiple in vitro and in vivo studies have demonstrated that metformin can impede the growth of thyroid cells and different types of TC cells. Furthermore, patients with Τ2DM treated with metformin have a smaller volume of thyroid tissue and a lower risk of incident goiter, thyroid nodules, and TC ,85,86. HIn some studies, adverse effects of antidiabetic drugs on thyroid function have also been observed. Metformin is a safe and effective oral hypoglycemic agent; however, it may affect thyroid function through complex pathways. Several studies suggest a TSH-lowering effect of metformin in individuals with T2DM and overt or SCH, independently of receiving TH replacement therapy, while it has no significant effects on serum levels of FT3 and FT4 ,91. MetfExogenous insulin administration can influence thyroid function in patients with T2DM by negatively regulating TRH and TSH secretion, increasing serum T4 levels, and decreasing T3 levels . RegardiRegarding incretin mimetics, animal  and humaIn patients with GO, large-dose corticosteroids are occasionally administered, which can exacerbate glucose intolerance or induce DM. Therefore, a careful assessment of the risk/benefit balance of their use in such cases is needed . In contAn increased prevalence of TD is observed among patients with T2DM, and a rise in the incidence of TC has been suggested among women with T2DM. The coexistence of these endocrinopathies seems to aggravate glycemic control and negatively affect thyroid function. In addition, specific diabetes drugs have positive or negative effects on TD that should be taken into account in daily practice. The landscape of T2DM management has been completely transformed in the last decade, with the introduction of new classes of drugs into the treatment algorithm, while a number of agents are currently in the pipeline ,113,114.Furthermore, prospective studies with long follow-up periods are needed to assess the impact of simultaneously restoring thyroid function and achieving glycemic control on the future prognosis and the risk of complications from these correlated disorders. Currently, there are evidence gaps and, as a consequence, vagueness in guidelines on thyroid screening in patients with T2DM or regular monitoring of glycemic control in patients with TD. Taking into account the important clinical implications of the coexistence of T2DM and TD, proper screening and management strategies are needed for both endocrinopathies to ensure optimal patient care.


Non-operative management has been suggested as a therapy for uncomplicated appendicitis. Notwithstanding, the risk of missing an appendiceal tumor must be considered, being the surgical piece crucial to rule out neoplasms. Therefore, we aim to determine the incidence of appendiceal neoplasms in patients with acute appendicitis, tumor types and the importance of the anatomopathological study of the surgical piece.Retrospective study in which we described patients who underwent emergent appendectomy with histopathological findings of appendiceal neoplasms from January 2012 to September 2018. Descriptive analysis included demographic variables, diagnostic methods, and surgical techniques.2993 patients diagnosed with acute appendicitis who underwent an emergency appendectomy. 64 neoplasms of the appendix were found with an incidence of 2,14%. 67.2% were women, the mean age was 46,4 years (± 19.5). The most frequent appendiceal neoplasms were neuroendocrine tumors , followed by appendiceal mucinous neoplasms , sessile serrated adenomas , and adenocarcinomas . In 89,1% of the cases, acute appendicitis was determined by imaging, and 14% of cases were suspected intraoperatively. Appendectomy was performed in 78,1% without additional procedures.Appendiceal tumors are rare and must be ruled out in patients with suspected acute appendicitis. The incidence of incidental neoplasms is higher in this study than in the previously reported series. This information must be included in decision-making when considering treatment options for acute appendicitis.The online version contains supplementary material available at 10.1186/s12893-023-02183-4. Appendectomy has been widely accepted as first-line management for acute appendicitis in the absence of abscess formation or peritonitis –4. HowevPseudomyxoma peritoneum) [Tumors of the appendix are unusual entities, mainly diagnosed incidentally in the anatomopathological study of appendectomy pieces, and rarely suspected before or during a surgical procedure , 11–15. itoneum) , 21. Desitoneum) .These neoplasms correspond to less than 0,5% of all tumors of the gastrointestinal tract , 24, andAfter Institutional Review Board’s approval, a retrospective study composed of patients with diagnosed acute appendicitis who underwent emergency appendectomies at a tertiary referral hospital was conducted. Over 7,5 years between January 2012 and September 2018, 2993 clinical records were reviewed. Patients over 18 years old, and those who underwent appendectomy for acute appendicitis, were included. Patients with previously known diagnoses of an appendiceal tumor, those undergoing right hemicolectomy (RHC), and patients who underwent appendectomy for a different indication than acute appendicitis, were excluded. To clarify, individuals with an Alvarado rating ranging from 1 to 4 were discharged. Alvarado scoring ranging from 5 to 6, were sent to ultrasound examination and abdominal CT scans if needed. Individuals with an Alvarado rating of 7 to 10 were directly diagnosed with acute appendicitis and underwent appendectomy. Demographic variables (such as the age of presentation and gender), as well as diagnostic methods, surgical techniques (including the extension of the resection), and the histological type described in the pathology report, were analyzed using descriptive statistics. The follow-up on these patients was continued until the first postoperative control.Of the 2993 samples of appendectomies analyzed, 64 neoplasms of the appendix were found  reported in 27 patients , of which 62,9% of the cases were women Fig. , with anThe second most frequent tumor was mucinous neoplasm of the appendix (AMN) reported in 23 samples , of which 69,6% were women Fig. . The aveThe sessile serrated adenoma (SSA), considered a pre-malignant lesion, was the third most frequent appendiceal tumor in this series, reported in 12 cases . The mean age was 46 (± 15.6) years. Of the 12 SSAs, 9 cases (75%) were women . The ratio of women to men was 1:1, with an average age of presentation of 54 years. One of the cases was reported as a well-differentiated neoplasm, and the other was poorly differentiated. Neither of the tumors was suspected before the surgical procedure. In one case, non-tumor-free resection margins were reported. Table Our population shows that appendiceal tumors incidentally found in emergent appendectomies are significant, with an incidence of 2,14%, similar to those reported worldwide, between 0,2% and 2,5% , 25. FroCurrently, the treatment of acute appendicitis is controversial since most recent literature has shown that the conservative management of these patients with the use of antibiotics in selected cases, is a viable and promising non-invasive option , 5, 6. NDifferent studies evaluating the non-operative management of uncomplicated acute appendicitis, such as the APPAC  and the M. Enblad et al.  showed tAppendicular inflammatory masses can be presented in 2 to 6% of cases, with a higher risk of appendiceal neoplasms that may vary between 5,2 and 12% , 29. MorAmong the limitations of this study are the relatively small number of patients, its retrospective nature, and the lack of longer follow-up. Further prospective studies are needed to evaluate the impact of non-surgical management of appendiceal inflammatory processes in terms of morbidity and mortality of appendicular neoplasms.Tumors of the cecal appendix must be considered as differential diagnoses to be ruled out in patients with suspected acute appendicitis. Unfortunately, they do not present specific signs or symptoms and are not suspected or evidenced before, or during surgical intervention, so their diagnosis remains incidental after the anatomopathological analysis of the surgical samples obtained from appendectomies. Appendiceal tumors are infrequent. However, the increasing incidence reported in recent international series, including the current one, enhances the need for the surgeon to discard this pathology as a cause of acute appendicitis. The possibility of incidentally diagnosing appendiceal neoplasms should be considered when making decisions about the appropriate management for acute appendicitis, whether surgical or not, considering that the histopathological analysis of the surgical specimen should be used for diagnostic purposes and to determine the requirement for additional treatment.Below is the link to the electronic supplementary material.Supplementary Material 1


The highest levels of pH, ash, carbohydrate, fiber, and glycemic index were observed in raw samh seeds, and both germination and fermentation processes reduced these attributes to various degrees (p < 0.05). Germination increased the redness (a*), moisture content, essential and non-essential amino acids, potassium, zinc, phosphorous, stearic acid, and oleic and unsaturated fatty acids and reduced total solids, fat content, iron, zinc, calcium, magnesium, sodium, palmitic acid, and total saturated fatty acids of the samh seeds compared to the raw ones. Fermentation increased the total solid, acidity, fat, protein, calcium, magnesium, sodium, phosphorous, iron, zinc, palmitic acid, and total saturated fatty acids and reduced the a* value, moisture, non-essential amino acids, and total unsaturated fatty acids of the samh seeds compared to the raw ones. In conclusion, samh seeds are a rich source of nutrients that could generally be enhanced by germination and fermentation processes. The reported information facilitates strategies towards the application of these underutilized seeds in foods.This study investigated the effects of fermentation and germination on the physicochemical, nutritional, functional, and bioactive quality attributes of samh seeds. Regardless of the processing treatment, samh seeds were found to be a rich source of phenolic compounds, namely gallic acid (79.6–96.36 mg/100 g DW), catechol (56.34–77.34 mg/100 g DW), and catechin (49.15–84.93 mg/100 g DW), and they possessed high DPPH antiradical activity (65.27–78.39%). They also contained high protein content (19.29–20.41%), essential amino acids content , and unsaturated fatty acid content  and a low glycemic index (39.61–41.43). Fermentation and germination increased L*, b*, foaming capacity, oil absorption capacity (OAC), water absorption capacity (WAC), swelling power, microbial counts, antioxidant activity, total flavonoid content (TFC), total phenolic content (TPC), in vitro protein digestibility, protein efficiency ratio, and total essential amino acids and reduced water solubility, emulsion stability, tannin, and phytate contents compared to raw samh seeds ( Mesembryanthemum forsskalei Hochst) plant is considered a suitable candidate that could be exploited in food and pharmaceutical applications. Samh is a halophyte plant that grows naturally in the semi-arid zones of Northern Africa and the Middle East and in many countries around the world [Recent decades have witnessed increased interest in the production and utilization of gluten-free food products from underutilized plant sources to overcome the health concerns associated with gluten-containing foods and to feed the growing world population . In addihe world ,3. Samh he world . This hahe world  commonlyhe world . Traditihe world . Additiohe world ,6. Nutrihe world ,4,5,7,8.he world ,10,11. She world ,13,14,15he world . In addiPrior to consumption and use, samh seeds undergo several processing treatments to improve their sensory attributes and nutritional quality. Roasting and cooking are the main thermal processes of samh seeds ,16. RoasMesembryanthemum forsskalei Hochst) were harvested during the 2022 growing season from the Al-Jouf district, northern Saudi Arabia. The seeds were physically cleaned to remove dirt and impurities and stored in firmly closed containers at 4 °C until being used for germination, fermentation, and chemical analyses. For the preparation of raw flour, part of the samh seeds was ground into fine flour by a grinder , and the flour was stored refrigerated at 4 °C in polyethylene bags for further use. The chemical reagents and standards used were of analytical grade and were acquired from Sigma-Aldrich .The seeds of the samh plant  in covered Erlenmeyer flasks and mixed. After that, the blend was subjected to 24 h incubation at 37 °C in an orbital shaking incubator  to allow indigenous microorganisms  to grow and ferment the mixture . Before measurement, the device was calibrated using a white surface calibration plate. The CIE color parameters (lightness (L*), redness (a*), and yellowness (b*)) were recorded for each sample in triplicate.2O or refined sunflower oil, thoroughly mixed, and left for 30 min at room temperature. In weighed centrifuge tubes, the resultant blends were subjected to 10-min centrifugation at 2000× g followed by decanting of the supernatants. The difference between the sample’s initial weight and its weight after the water or oil addition had been emptied was used to determine OAC and WAC. The results are presented as grams of bound water or oil per gram of samh seed flour.According to the procedures outlined by Sosulski et al. , 1 g of The emulsifying abilities of raw, fermented, and germinated samh seed flour were assessed using Elkhalifa and Bernhardt’s  method ww/v) were blended in a high-speed lab mixer for 5 min, after which the mixture was decanted into a measuring cylinder (250 mL) and the foam volume was measured and used for calculation of the percentage of foaming capacity. The percentage of foam volume in the measuring cylinder remaining after 60 min of incubation was considered as the foaming stability.The foaming capacity (FC) and foam stability (FS) of the raw, fermented, and germinated samh seeds were determined using the technique of Figueroa-Gonzalez et al. . The sam3) was determined by dividing the sample weight by the sample measured volume. The swelling power and water solubility of the samh seed samples  were assessed using published methods [2O in pre-weighed centrifugal tubes and the blend was subjected to 30 min of heating at 90 °C. Then, the mixture was centrifuged for 15 min at 3000× g. After decanting the supernatant, the increase in the sample’s weight was determined as the swelling power. The soluble matter-containing decanted supernatant was dried in dishes that had already been precisely weighted, and the residual weight was then expressed as the water solubility.The bulk density of samh seed samples  was assessed using a documented procedure  with sli methods . BrieflyThe standard AOAC official methods numbers 942.15, 981.12, 925.09, 940.09, 942.05, 992.15, 991.43, and 960.39  were useThe total viable count (TVC) and mold and yeast counts were assessed using Oxoid nutrient agar and potato dextrose agar, respectively . Optimal3COO buffer (pH 5.2), 1 mL of pancreatin (243 mg/mL), and 56 μL amyloglucosidase (260 U/mL) were added to the mixture followed by 180-min incubation at 37 °C under continuous shaking. At 20 min intervals, 100 μL aliquots were taken and added to 1 mL ethanol followed by 10-min centrifugation at 8000× g. Then, a glucose determination kit, GOD-PAP , was used for the determination of glucose content spectrophotometrically at 500 nm . A hydrolysis curve was generated using the absorbance against the time and used for the calculation of hydrolysis and glycemic indexes.The glycemic index of raw, fermented, and germinated samh seeds was estimated following a previously reported method  with somg. The extraction process was repeated three times, and the obtained supernatants were combined, concentrated in a rotary evaporator at 37 °C, filtered with filter membranes (0.45 μm), and then retained for further use.Prior to the analysis of the bioactive properties of raw, fermented, and germinated samh seeds, extracts were prepared as reported previously . Concisev/v) was mixed with 10 mL of Na2CO3 (700 mM) and ddH2O in the total volume of 25 mL. After an additional 1 h of incubation at room temperature, the absorbance was read at 750 nm  and the results were reported as mg gallic acid equivalents per 100 g (mg GAE/100 g) using the standard curve generated with gallic acid (0–200 mg/mL).The total phenolic content (TPC) of the samh seed extracts was assessed by using the Folin–Ciocalteu (FC) reagent colorimetric method . After 52, 0.6 mL of AlCl3 (750 mM) was added and the reaction mixture was subjected to further incubation for 6 min at room temperature followed by the addition of 2 mL of NaOH (1 M) and ddH2O to a final amount of 5 mL. The absorbance of the pink color solution was recorded at 510 nm, and the results are presented in mg catechin equivalents per 100 g (mg CE/100 g) samh seeds.The total flavonoid content (TFC) of raw, fermented, and germinated samh seed extracts was assessed using Dewanto et al.’s  method. The individaul phenolic compounds of the raw, fermented and germinated samh seed extracts were separated, identified, and quantified using the HPLC method as described previously .3COO buffer (pH 3.6), 0.01 M 2,4,6-Tri (2-pyridyl)-s-triazine (TPTZ) solution, and 0.02 M FeCl3.6H2O at a ratio of 10:1:1 (v:v)), and the reaction was allowed to proceed at 37 °C for 30 min. After that, the absorbance was recorded at 593 nm, and the results were specified as the amount of substance produced during the reduction of Fe3+-TPTZ to Fe2+-TPTZ. For the ABTS assay, 10 mL of 7 mM ABTS was combined with 10 mL of K2S2O8 (2.4 mM) in the dark for 2 days at room temperature to make the ABTS reagent. Immediately before use, 5 mL of the ABTS reagent was diluted with 100 mL ethanol to attain an absorbance of 0.70 at 734 nm. Then, 50 μL of sample extract was added to 2 mL of the ABTS solution, which was combined with 1.95 mL of ethanol to make a final volume of 4 mL. After 6-min incubation at room temperature, the absorbance was recorded against a blank at 734 nm, and the difference in the absorbance between the sample extract and the blank was used for calculation of the ABTS inhibition percentage.The antioxidant activity of the raw, fermented, and germinated samh seeds extracts was measured by using 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging , ferric 3 solution until a brownish–yellow tint developed and remained for 5 min. Then, the results were recorded and used for the calculation of phytate based on the fact that an equal molar (1:1) ratio of Fe to phytate exists. The concentration of FeCl3 was 1.04% w/v, and the results were converted to mg phytic acid/g dry matter. For the tannin assay, 1 g of samh seeds  was mixed with 40 mL of 10% aqueous ethanol, and the mixture was heated in a water bath for one to two hours while being occasionally stirred. After filtering, the mixture was diluted with methanol to 50 mL. After that, 1 mL of the extract was mixed with 5 mL of Folin–Denis reagent, 10 mL of 35% saturated sodium carbonate, and 100 mL of distilled water, followed by 30-min incubation of the reaction mixture at room temperature. Then, the absorbance of the mixture was recorded at 760 nm, and results are reported as mg tannic acid equivalent (mg TAE/g sample).The antinutritional factors of the raw, fermented, and germinated samh seeds were assessed by spectrophotometric measurement of phytate content  and tann2O, drying using a rotary evaporator, and reconstitution with 0.1 N HCl (2.5 mL). After derivatization and membrane filtration (0.22 μm), a 20 μL sample was introduced to the HPLC system (Agilent 1260) furnished with an Eclipse Plus C18 column (4.6 mm × 250 mm × 5 μm) and DAD (338 nm) and fluorescence (260–340 nm excitation/305–450 nm emission) detectors. The mobile phase contained sodium phosphate buffer pH 7.8 (A) and acetonitrile: methanol: water 45:45:10 (B) was run at a 1.5 mL/min flow rate. A linear gradient mode of 98% (A) and 2% (B) from 0–33.40 min, 43% (A) and 57% (B) from 33.40–33.50 min, 0% (A) and 100% (B) from 33.50–39.40 min, and 2% (A) and 98% (B) from 39.40–40.0 min was used, and a constant temperature of 40 °C was maintained for the column. The equations described by Mohapatra et al. [The analysis of the amino acid profile of the raw, fermented, and germinated samh seeds was carried out as described by Mohapatra et al. . The hyda et al.  were useg followed by filtration and quantification of the nitrogen content in the TCA soluble fraction by using the micro-Kjeldahl method [The in vitro protein digestibility of the raw, germinated, and fermented samh seeds was assessed after 2 h hydrolysis at 37 °C using a reaction mixture composed of 0.4 g of samh sample and 1 mg of pepsin in 15 mL of 0.1 M HCl . After tl method . Then, t2SO4 at 220 °C for 30 min in a hot plate digestion system. After cooling to room temperature, 1 mL H2O2 was added, and digestion was continued at 400 °C until a clear solution was achieved (2 h). After digestion, Whatman #42 filter paper was used for filtration of the mixture, and the filtrate was diluted to a 50 mL final volume with ddH2O. Then, all micro- and macro minerals in the digested sample filtrates were analyzed by using an ICP-OES  except sodium and potassium, which were determined by a flame photometer  and phosphorus which determined the content calorimetrically.The free mineral content of the raw, fermented, and germinated samh seeds was analyzed using the method stated by Milani et al. . ConciseOil was extracted from the raw, fermented, and germinated samh seeds samples by using n-hexane in a Soxhlet oil extraction system for 8 h followed by methylesterification with methanolic boron trifluoride (BF3-MeOH) to form fatty acid methyl esters (FAMEs) as described previously . The FAMp < 0.05. All results are presented as the mean ± standard deviation.Triplicate replicates were conducted for each treatment, the samples were analyzed in triplicate for each experiment, and the data were subjected to statistical analysis using SPSS software . Differences among the experimental groups were analyzed using ANOVA and Duncan’s multiple range test (DMRT), and significance was accepted at p < 0.05). In comparison to the control (raw samh seeds), germination increased the L*, a*, and b* values, whereas fermentation increased the L* and b* values and reduced the a* value (p < 0.05). The uppermost levels of L* and b* were seen in the fermented samh seeds, whereas the highest value for the a* value was noted in the germinated samh seeds. The increase in L* and b* of the germinated and fermented samh seeds is likely due to the enzymatic breakdown of intact colorant compounds during germination and fermentation processes. The surface color of the samh seed coat varies between yellow to deep red, and this might be an additional reason for the increased redness and yellowness of samh seeds during germination due to increased migration of the seed coat color to the endosperm where subsequent drying and milling processes concentrate these pigments in the seeds. The reduction in the redness of fermented samh seeds is probably due to the breakdown of the seed pigment during the fermentation process. Previous reports have shown varied changes in the color attributes of seeds and grains during germination and fermentation processes. For example, Tian et al. [The changes in the surface color and functional properties of samh seeds as influenced by the germination and fermentation process are shown in n et al.  reportedn et al.  reportedp < 0.05). Fermentation treatment greatly increased all of the assessed functional properties to the highest values, except for emulsion stability and water solubility, which were reduced in the fermented samh seeds compared to the raw ones (p < 0.05). The germination process considerably (p < 0.05) reduced the emulsion activity, foaming stability, and water solubility to the minimum values compared to that of the raw and fermented samh seeds. The alterations in the functional characteristics of the germinated and fermented samh seeds are probably due to the enzymatic and non-enzymatic structural modifications of macromolecules during the fermentation and germination processes. The rise in WAC might be due to the degradation of macromolecules such as polysaccharides and fiber and changes in protein content and quality, thereby increasing the hydrophilic sites and trapping more water within the flour matrix [Moringa oleifera seeds [Regarding the functional properties, both treatments significantly increased the oil absorption capacity (OAC), water absorption capacity (WAC), swelling power, and foaming capacity and decreased the water solubility and emulsion stability compared to the raw samh seeds . The fermentation process significantly increased the total solids, acidity, TVC, yeast and mold count, fat, and protein content to maximum values and reduced the pH, moisture, fiber, and estimated glycemic index of the samh seeds to the minimum values (p < 0.05). The highest values of pH, ash, carbohydrates, fiber, and glycemic index were observed in the raw samh seeds, and both process reduced these attributes to various degrees (p < 0.05). In comparison to the control, germination (p < 0.05) decreased the total solids and fat and augmented the moisture content of the samh seeds. The microbial count (TVC and yeast and mold count) was greatly increased by the fermentation and germination processes compared to the control. The reduction in pH and increase in acidity in the germinated and fermented samh seeds might be due to the increase in TVC and yeast and molds, and their increased metabolic activity could lead to the formation of organic acids. The increase in protein content in the germinated and fermented samh seeds is likely due to the synthesis of enzymes, the degradation of antinutritional factors, the synthesis of new proteins, and the digestion of insoluble storage proteins into simple soluble proteins [The results of the total solids, acidity, pH, total viable count (TVC), yeast and mold count, proximate composition, and estimated glycemic index of the samh seeds as influenced by the fermentation and germination processes are presented in proteins ,45. In aproteins ,41,45,48proteins ,50. It iproteins . In nutrproteins , and basproteins . In accoproteins .p < 0.05) enhanced the phenolic compounds, TFC, and TPC of the samh seeds, and consequently, the antioxidant activity as assessed by the ABTS, DPPH, and FRAP approaches was also greatly (p < 0.05) improved by both treatments. The highest TFC was observed in the germinated seeds followed by the fermented seeds, whereas the lowest values were observed in the raw seeds (p < 0.05). The highest levels of TPC, ABTS, DPPH, and FRAP were found in the fermented seeds followed by the germinated seeds, whereas the lowest values were observed in the raw samples (p < 0.05). Among the phenolic compounds  reduction in the tannin and phytate contents compared to the control. The uppermost levels of tannin and phytate were witnessed in the raw samh seeds followed by the fermented and then germinated seeds. The highest decline in phytate and tannin was evident in the germinated seeds. The decrease in phytate and tannin content in the fermented and germinated samh seeds could be ascribed to the augmented metabolic activity of tannin- and phytate-degrading enzymes such as tannin acyl hydrolases, phosphatases, phytases, and polyphenol oxidases during the germination and/or fermentation of samh seeds [Fermentation and germination treatments led to a substantial  augmented the essential and non-essential amino acids of samh seeds to the highest levels compared to the raw and fermented samples, which might be associated with a high reduction in antinutritional factors in germinated seeds (p < 0.05) all essential amino acids of the samh seeds compared to the control, which might be ascribed to the production of essential amino acids by fermenting microbes, especially lactic acid bacteria [p < 0.05). The reduction in non-essential amino acids during the fermentation of the samh seeds could be due to the metabolisms of amino acids by fermenting microbes [The effect of germination and fermentation on the amino acids profile and in vitro protein digestibility of the samh seeds is illustrated in ed seeds . The upsbacteria . Howevermicrobes . Similarmicrobes . In contmicrobes  and Afrimicrobes . The totmicrobes . These fp < 0.05) enhanced by germination and fermentation treatments, with the highest level of protein digestibility being observed in the fermented seeds (57.28%) followed by the germinated seeds (51.04%), whereas the lowest value was seen in the raw seeds (45.63%). The changes in the in vitro protein digestibility of the samh seeds were shown in a 25.53% and 11.86% increase in the fermented and germinated seeds, respectively. The increase in the protein digestibility of the samh seeds following germination and fermentation treatments is probably due to the modification of native proteins, alteration of the interaction between proteins and antinutritional factors, and the availability of protein functional groups that are susceptible to enzymatic hydrolysis [p < 0.05) from 0.36 in the raw seeds to 0.49 and 1.06 in the fermented and germinated seeds, respectively, indicating better biologically available proteins in the germinated samh seeds followed by the fermented samh seeds. Similarly, an improved protein efficiency ratio was reported in germinated sorghum grains [The in vitro digestibility of samh seeds protein was considerably  augmented the levels of calcium, magnesium, sodium, and phosphorous to the highest levels compared to the control. The uppermost level of potassium was apparent in the germinated seeds followed by the fermented seeds. Germination reduced the levels of calcium, magnesium, and sodium and increased the phosphorous content of the samh seeds compared to the control (p < 0.05). Among the micro-minerals, the uppermost levels of chromium, iron, and zinc were seen in the fermented seeds, whereas the uppermost amounts of boron, copper, and manganese were seen in the raw seeds. Fermentation treatment increased the levels of the assessed micro-minerals, except boron and copper, which were reduced by fermentation compared to those of the raw seeds. Germination treatment reduced the levels of the assessed micro-minerals, except zinc and chromium, which increased after germination compared to the untreated control (p < 0.05). Generally, fermentation increased the mineral content of the samh seeds, with a few exceptions, whereas germination reduced most of the assessed minerals, except potassium, chromium, and zinc, which were higher in the germinated seeds compared to the raw ones. The increase in minerals following the fermentation treatment of the samh seeds is probably due to the degradation of antinutritional factors and the liberation of bound minerals into soluble forms during the fermentation process [The effect of fermentation and germination on the mineral content of raw, germinated, and fermented samh seeds is shown in  process ,48,59. H process . Previou process ,48,59,65p < 0.05). The most dominant fatty acids in the raw, fermented, and germinated samh seeds were linolenic acid followed by oleic and palmitic acids, which is in agreement with the results of Al-Jassir et al. [The results of the fatty acid composition of samh seeds as influenced by fermentation and germination treatments are presented in r et al. , who star et al. ,66. Durir et al. ,68. Varir et al. ,67,68. OIn this study, the impacts of fermentation and germination on the functional, nutritional, and health quality attributes of samh seeds were evaluated. Samh seeds are rich in bioactive compounds, protein, essential amino acids, and unsaturated fatty acids and could be deliberated as a promising candidate for developing functional foods. Germination and fermentation treatments can enhance the nutritional value of samh seeds by increasing their bioactive properties , in vitro protein digestibility, protein efficiency ratio, and total essential amino acids and reducing antinutritional factors (tannin and phytate), carbohydrate content, and glycemic index. In addition, both processes could also improve the functional characteristics of samh seeds by their increasing oil absorption capacity (OAC), water absorption capacity (WAC), foaming capacity, and swelling power. Germination also enhanced the nutritional quality of the samh seeds by increasing potassium, zinc, phosphorous, and oleic and unsaturated fatty acids and reducing fat content, palmitic acid, and total saturated fatty acids. Fermentation also enhanced the nutritional quality of samh seeds by increasing protein, calcium, magnesium, sodium, phosphorous, iron, zinc, palmitic acid, and total saturated fatty acids. However, it reduced the moisture, non-essential amino acids, and total unsaturated fatty acids of the samh seeds. Overall, samh seeds are a rich source of nutrients and bioactive compounds, which are mostly enhanced by germination and fermentation processes. Fermented and/or germinated samh seeds can be used in food applications as nutritious and functional food, adding more value to these underutilized wild plant seeds.


Sugarcane mosaic virus (SCMV) and Sugarcane streak mosaic virus (SCSMV) are the primary pathogens responsible for mosaic disease in sugarcane-growing regions of India. Sugarcane bacilliform virus (SCBV), causing leaf fleck disease, is also often found in mixed infections with mosaic symptoms. The study aimed to identify new sources of resistance by screening sugarcane germplasm for resistance to SCMV, SCSMV, and SCBV. The screening was carried out under high inoculum using the infector row method in both plant and ratoon crops. Out of 129 genotypes tested, only 8 were found to be free of mosaic viruses, indicating a rare occurrence of resistant sources. The study revealed that mosaic disease is widespread, with nearly 95% of tested varieties/genotypes being infected with mosaic viruses. SCMV, SCSMV, and SCBV were detected in 121 out of 129 genotypes using the RT-PCR and PCR assays. Based on their response to the viruses, the tested genotypes were categorized into different resistance grades: highly resistant (grade 1), resistant (grade 2), moderately resistant (grade 3), susceptible (grade 4), and highly susceptible (grade 5). The results of the study provide valuable information about elite resistance resources that can be used for the prevention and control of mosaic disease. These resistant genotypes could also serve as potential donors for mosaic and leaf fleck disease resistance in breeding programs.Sugarcane mosaic and leaf fleck diseases are significant viral diseases affecting sugarcane crops in India. The use of resistant sugarcane varieties is considered the most economical and effective approach to manage viral diseases, especially in vegetatively propagated crops such as sugarcane.  Saccharum and family Poaceae. It is cultivated as a vital field crop for sugar and bioenergy production and is grown in more than 110 countries worldwide, covering about 26.34 million hectares and yielding approximately 1,859.39 million tons. Approximately 80% of the world’s sugar demand is met by this crop. Sugarcane has had a historical presence in Asian farmlands since ancient times and continues to occupy a significant place in agriculture and the economy. Major sugarcane-producing countries include India, Brazil, China, Thailand, Pakistan, Mexico, Colombia, Indonesia, the Philippines, and the United States. India stands out as the second-largest producer, accounting for approximately 20% of the world’s cane production, covering 5.15 M ha–1 with a production of 405.39 Mt , Sugarcane streak mosaic virus (SCSMV), and Sorghum mosaic virus (SrMV). These viruses infect sugarcane plants and lead to the manifestation of mosaic-like symptoms, which involve the development of irregular patterns of light and dark green on the leaves  have been reported in India. These mixed infections can lead to more severe symptoms, such as dwarfing and necrosis, resulting in substantial production losses for susceptible sugarcane cultivars , Anakapalli, for the evaluation of mosaic and leaf fleck resistance during the period 2021–2022 (plant crop) and 2022–2023 (ratoon crop) crop growing seasons by the infector row method. Entries were planted in two rows of 5 m in length, and 25 three-budded setts were planted in each row. The experiment was laid out in an augmented block design with a susceptible check, 2009A 107, repeated after three entries. Resistance in all the entries was confirmed through RT-PCR and PCR at the formative and maturity stages. Entries that were found to be highly resistant under field conditions in plant crops were planted in a glass house in the next season along with the susceptible check and were inoculated with sugarcane mosaic-infected sap 45 days after planting. Furthermore, resistance was confirmed with RT-PCR and PCR.The list of 129 sugarcane genotypes evaluated for mosaic and leaf fleck resistance studied in the present study is given in Based on the disease incidence, tested genotypes were categorized according to Disease index scale for mosaic disease of sugarcaneThe genotypes that were found to be highly resistant in plant crops were planted in a glass house and mechanically inoculated with crude sap from RT-PCR-determined SCMV and SCSMV and PCR-determined SCBV-infected sugarcane leaves extracted in 0.1 M phosphate buffer, pH 7.0 .Total DNA was extracted from the samples collected by following the CTAB technique . Leaf saPrimers used for PCR amplification2, 2 μl dNTPs, 10 pmol forward and reverse primer, 1.25 units Taq DNA polymerase , and sterile Milli-Q water. A thermocycler  was used to carry out the PCR reaction at an annealing temperature of 59°C for 45 s. The PCR products were separated by electrophoresis and visualized under a gel documentation system .The most conserved RT/RNase H domains and sequences have been widely employed in Badnavirus taxonomy and viral detection. Primers specific to SCBV were used in the PCR reaction to target the RT/RNase H coding region of the genome, which is a region of 794 bp in size. A PCR reaction of 25 μl includes 2 μl of DNA, 2 μl Taq buffer, 2 μl MgclAll the samples collected from 129 sugarcane genotypes at vegetative and grand growth stages were subjected to RNA extraction. In total, 100 mg of fresh sample was powdered using liquid nitrogen to ensure rapid and thorough disruption of cellular structures. The powdered sample is then transferred to a microcentrifuge tube treated with DEPC and mixed with 1 ml of TRI reagent. The sample and TRI reagent mixture are vigorously shaken to ensure proper mixing and then incubated at 4°C. This allows time for the nucleoprotein complexes to dissociate and for RNA to be released from the cells. After incubation, the mixture is centrifuged at a high speed of 12,000 rpm and kept at a low temperature (4°C) for 10 min. The supernatant (liquid phase) containing the RNA was transferred to a new tube, and 200 μl of chloroform was added. The mixture was shaken to create an emulsion and then incubated at room temperature. This step helps to separate different phases within the solution. The mixture was centrifuged again at 12,000 rpm and 4°C for 15 min. This step leads to the separation of the solution into three distinct phases: a lower organic phenolic phase containing proteins, an inter-phase containing DNA, and an upper colorless aqueous phase containing RNA. The upper aqueous phase, which contains the RNA, is carefully transferred to a new tube. Then, 500 μl of isopropanol is added to the RNA-containing solution, causing the RNA to precipitate out of the solution. The mixture is centrifuged again to form a pellet and washed thrice with ethanol. The pellet is air-dried for 10 min to remove any residual ethanol. Then, the RNA pellet is dissolved in 40 μl of molecular-grade water . The res2O, 2 μl RT buffer, 0.25 μl Oligod (T), 0.125 μl RT enzyme, 0.25 μl RNase inhibitor, and 2 μl RNA template. The reaction mixture was incubated at 70°C for 5 min, followed by inactivation of the enzyme at 37°C for 90 min, and termination of the reaction at 72°C for 10 min. A PCR reaction volume of 25 μl consisting of 2 μl of cDNA, 2 μl of Taq buffer, 2 μl of Mgcl2, 2 μl of dNTPs, 10 pmol of both forward and reverse primers, 1.25 units of Taq DNA polymerase , and sterile Milli-Q water makeup to the final volume. Thermocycler  was used to carry out the PCR reaction with an annealing temperature of 65°C for 45 s. The PCR products were separated by electrophoresis and visualized under a gel documentation system .The first-strand cDNA synthesis kit (Thermo Scientific) was used for the synthesis of cDNA. The reverse transcription was performed in a 10 μl reaction mixture that contained 2 μl 10 mM dNTPs, 4.375 μl ddHRepresentative samples of the amplicons from all three viruses were chosen for sequencing. Amplicons were purified and sequenced by Sanger’s dideoxy chain termination method. After obtaining the sequencing data, nucleotide homology searches were performed using the BLASTN algorithm on the NCBI database. The sequences were aligned using the MUSCLE algorithm . A phyloScreening of 129 elite sugarcane genotypes for broad-based resistance to mosaic was taken up in both plant crop and ratoon under natural field conditions, along with a susceptible check, 2009A 107, at the experimental field of RARS, Anakapalli. Among the tested (129) genotypes, 8 genotypes  were scored as highly resistant (grade 1) with null disease index, eight genotypes  were scored as resistant (grade 2), 59 genotypes were scored as moderately resistant (grade 3), 39 genotypes were scored as susceptible (grade 4), and 15 genotypes were scored as highly susceptible (grade 5) to mosaic disease in plant crop . The susField resistance of sugarcane genotypes based on visual grading was further confirmed by RT-PCR/PCR detection of SCMV, SCSMV, and SCBV in symptomatic and asymptomatic leaf samples from 129 sugarcane genotypes. The 891, 690, and 794 bp expected fragments of the SCMV and SCSMV coat protein genes and the RT-RNase H region gene were not amplified in the eight samples obtained from sugarcane genotypes, which were graded as highly resistant (grade 1) to all three viruses in the plant crop. These expected amplicons were present in all resistant to highly susceptible tested genotypes rated as grades 2–5 . GenotypThe genotypes 2009A 107, 93A 145, 87A 298, 206A 223, 2018 202, 2018A 107, 2017A 497, 2018A 135, Co 997, Khakai, CoC 671, Co 62399, and Co 86032 displayed severe mosaic symptoms and could be quickly identified as diseased even from a few yards away. Younger leaves, often the center leaf, in particular, displayed severe mosaic symptoms such as green streaks and mosaic symptoms throughout the leaf lamina in all tested genotypes. The typical mosaic green inlays/streaks were found to be confined to the proximal part of the leaf lamina in various genotypes, including 2000A 225, 98A 163, 2010A 229, and CoLk 14203, whereas the distal end was devoid of the mosaic symptoms.At 2–3 months of age, the sugarcane genotypes 2016A 254, 2015A 309, 81A 99, 2006A 223, Co 86032, Co 15024, Co 740, and CoC 671 were reported to exhibit mosaic symptoms; however, when the crop was checked again at sixth and seventh months, there were no such symptoms. The symptoms progressively subsided as the leaves grew older. Similar kinds of gradual disappearing symptoms with the aging of the leaves were observed by The representative nucleotide sequences of SCMV, SCSMV, and SCBV were subjected to BLAST analysis and shared maximum nucleotide homology with most of the Indian isolates. Phylogenetic analysis was performed with the nucleotide sequences of SCMV, SCSMV, and SCBV, along with corresponding sequences available in NCBI. The isolates of SCMV, SCSMV, and SCBV clustered with most of the other Indian and foreign isolates .Mosaic and leaf fleck diseases of sugarcane are systemic diseases most commonly transmitted through infected seed cane. The most economical, practical, and effective strategy for controlling mosaic and leaf fleck diseases is to identify, develop, and cultivate sugarcane varieties that are resistant to the diseases. Resistance to multiple viruses is crucial because it can provide broader protection against different strains of the disease and reduce the chances of new infections or virus evolution. Breeding disease-resistant cultivars begins with the exploration and use of a genetic resource. Furthermore, systematic studies are required to understand the single or compound infection of viruses to address the resistance to mosaic and leaf fleck in India. In the current study, resistance to SCMV and SCSMV, the two major causes of mosaic disease, and SCBV, the cause of leaf fleck disease, was identified in 129 new elite sugarcane varieties/clones in both plant and ratoon crops. The tested genotypes exhibited typical mosaic symptoms varying with disease index ranging from 5.50 to 100 and 6.75 to 100% in plant and ratoon crops, respectively. Similar results were reported by To confirm the presence or absence of the virus, RT-PCR/PCR detection was utilized. Eight genotypes showed tolerance to all three viruses. These findings offer a superior resistance source for the efficient prevention and management of mosaic and leaf fleck diseases, and they could be used as a guide for commercial cultivars. Two viruses, SCMV and SCSMV, are the two major viruses that can cause mosaic disease, and SCSMV has become a major pathogen responsible for mosaic disease in the sugarcane-growing regions of India . FurtherThe resistance mechanism that plants have against viruses can be compromised by several factors. These include the presence of new virus strains, favorable environmental conditions that aid in virus multiplication and infection, the presence of virus-carrying vectors nearby, and more. Among the three viruses, SCSMV was found to be the most dominant, with the distribution frequency being as follows: SCSMV > SCBV > SCMV. Infection from a combination of viruses has the potential to be more virulent and harmful than infection from a single virus. According to reports, a crop suffers more damage from a combined SCMV and SrMV infection than from a solo infection with one of these viruses . The comvia our efforts, we were able to identify eight tolerant genotypes with widespread resistance to mosaic and leaf fleck, including 2017A 553, 2017A 416, 2017A 517, 2016A 379, Co7717, Co S 767, Baragua, and Co 13034, which may be used as donors for breeding new varieties that are resistant to the mosaic and leaf fleck viruses. In the current investigation, we also found significant disease susceptibility in sugarcane genotypes and widespread disease occurrence in the majority of genotypes. The prevalence of SCSMV was found to be greater than that of SCMV and SCBV. All three viruses can co-infect, causing severe symptom manifestation and degeneration. The screened germplasms may offer prospective sources of resistance for the development of resistant cultivars to the sugarcane mosaic and leaf fleck diseases.The most practical and economical method for managing any viral disease in a crop that is propagated vegetatively, such as sugarcane, is host-plant resistance. Despite the fact that very few researchers have found the origins of mosaic and leaf fleck resistance to date, the majority of their investigations have only evaluated a small number of genotypes. Contrarily, https://www.ncbi.nlm.nih.gov/genbank/, OR099818, OR099811, and OR060699.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: GVa: Investigation, Writing - original draft. VMK: Supervision. PKV: Conceptualization, Methodology, Resources. BB: Visualization. GVi: Visualization.


Gliomas are the prevalent forms of brain cancer and derive from glial cells. Among them, astrocytomas are the most frequent. Astrocytes are fundamental for most brain functions, as they contribute to neuronal metabolism and neurotransmission. When they acquire cancer properties, their functions are altered, and, in addition, they start invading the brain parenchyma. Thus, a better knowledge of transformed astrocyte molecular properties is essential. With this aim, we previously developed rat astrocyte clones with increasing cancer properties. In this study, we used proteomic analysis to compare the most transformed clone (A-FC6) with normal primary astrocytes. We found that 154 proteins are downregulated and 101 upregulated in the clone. Moreover, 46 proteins are only expressed in the clone and 82 only in the normal cells. Notably, only 11 upregulated/unique proteins are encoded in the duplicated q arm of isochromosome 8 (i(8q)), which cytogenetically characterizes the clone. Since both normal and transformed brain cells release extracellular vesicles (EVs), which might induce epigenetic modifications in the neighboring cells, we also compared EVs released from transformed and normal astrocytes. Interestingly, we found that the clone releases EVs containing proteins, such as matrix metalloproteinase 3 (MMP3), that can modify the extracellular matrix, thus allowing invasion. Gliomas are heterogeneous brain cancers that derive from glial cells ,4,5,6,7.On the other hand, new therapies are also required because gliomas, especially the high-grade ones, are still fatal, in spite of well-defined therapies based on surgery, radiotherapy and chemotherapy ,16,17, aThus, in the present study, we decided to perform a proteomic comparison between the A-FC6 cell line and normal primary astrocytes, in order to detect molecular pathways that can be affected by transformed phenotype. We also focused on proteins encoded on the partially duplicated chromosome 8. Moreover, we analyzed the extracellular vesicles (EVs) released by both normal and transformed astrocytes. It is, indeed, now widely accepted that both normal and cancer cells are able to release EVs through which they transfer a variety of molecules  to other cells, thus inducing epigenetic modifications of their behavior . It has The experiments described in the present paper have been performed on normal astrocytes and on the A-FC6 clone that had been stored as frozen material; normal astrocytes had been purified from cortices of rat fetuses and the astrocyte clone had been established as previously described . Those eg for 5 min and the cell pellet was resuspended in an appropriate volume of “Nuclei Buffer”  containing protease inhibitors . The cells were then homogenized with a Dounce homogenizer in an ice bath and stored at −80 °C until use. Protein concentration was determined by the Bradford protein assay, and aliquots containing 20 µg of total protein were used in triplicate for proteomic analyses. The total proteins were alkylated, reduced and digested using trypsin, as previously described [When the cells reached sub-confluence, the Fetal Calf Serum (FCS)-containing medium was substituted with FCS-free medium, and cells were cultured for 24 h more. The medium was then removed, and three PBS washes were performed. The cells were detached with a scraper and suspended in PBS. The cell suspension was then centrifuged at 500× Rattus norvegicus reference database from UniProt. Trypsin was defined as protease and two missed cleavages were allowed for the database search. The option first search was used to recalibrate the peptide masses within a window of 20 ppm. For the main search, peptide and peptide fragment mass tolerances were set to 4.5 and 20 ppm, respectively. Carbamidomethylation of cysteine was defined as a static modification. Protein acetylation at the N-terminus and oxidation of methionine were set as variable modifications. A protein was considered identified if it was present in all three replicates analyzed.The mass spectra obtained were used for protein identification, using Maxquant software , searched against p-value”, was used for subsequent analyses.The Excel file obtained, containing the number of total proteins identified and their relative abundance, determined by means of the label-free quantification (LFQ) parameter, calculated by the analysis software based on the intensity of the mass spectra and “g for 10 min and then at 4000× g for 15 min. The supernatant was centrifuged at 105,000× g  for 90 min at 4°C. The pellet containing microvesicles (MVs) was resuspended in PBS, and protein concentration was determined using a Qubit Protein assay kit .Astrocytes were cultured in Dulbecco Modified Eagle Medium/Ham’s F12 (DMEM/F12) (2:1), until sub-confluence; cells were then washed twice in phosphate-buffered saline (PBS), pH 7.5, and incubated with FCS-free medium for 24 h. Conditioned media were centrifuged at 2000× Twenty-microgram samples of total proteins, prepared as described above, were loaded on 10% polyacrylamide-SDS denaturing gels. After electrophoresis, samples were blotted onto polyvinylidene difluoride (PVDF) membranes . Transfer of proteins to the membrane and concentrations of the samples were visualized by staining the membranes with Ponceau red for 10 min. Finally, membranes were immunostained with a mouse monoclonal anti-MMP3 antibody (anti-MMP-3 (Ab-1) Mouse mAb (55-2A4) IM36 from Calbiochem, CA, USA). The secondary anti-mouse antibody was from Promega .p-value is reported on the ordinate.Comparative proteomic analysis of proteins extracted from the A-FC6 clone versus those from primary astrocytes identified a total of 903 proteins . As showThe 101 upregulated and the 46 uniquely expressed proteins evidenced in A-FC6 cells, as well as the 154 proteins downregulated in A-FC6 and the 82 uniquely expressed proteins of primary astrocytes, were highlighted by using the FunRich tool . InteresR. norvegicus genome contains 43,258 genes and encodes 68,976 proteins. In particular, chromosome 8 contains 2222 genes and encodes 6% of all proteins . Since the cytogenetic analysis of the A-FC6 clone showed the presence, in 100% of the observed metaphases, of an isochromosome 8 (i(8q)), due to duplication of the long arm of chromosome 8, we verified whether the expression of the genes present in chromosome 8 was significantly different between primary astrocytes and the A-FC6 clone. Surprisingly, no significant variation in the amount of proteins encoded by chromosome 8 was found when the two astrocyte cell populations were compared, suggesting that some compensatory mechanism of gene expression might be activated in cancer cells. However, as shown in the Venn diagram reported in The The comparative proteomic analysis was also performed on the extracellular vesicles (EVs) released from primary astrocytes and A-FC6 cells. Collectively, a total of 709 proteins were identified . InteresInterestingly, eight of the proteins uniquely identified in EVs derived from A-FC6 clone, namely Integrin Subunit α 8 (Itga8), Mmp13, Serpine2, Phospholipase C Delta 1 (Plcd1), Embigin (Emb), Mmp3, Complement defense (Cd59), and Polyunsaturated fatty acid lipoxygenase (Alox15), were also identified as unique in A-FC6 cell lysate.As a confirmation of the data reported above, we analyzed EVs from both primary astrocytes and A-FC6 cells by Western blot analysis  with antThe fundamental role of astrocytes in brain functions has been recognized for many years. First of all, they give metabolic support to neurons; they contain, for example, glycogen that is used when a special expense of energy is required for neuronal functions, as occurs during learning and memory processing ,26,27. ILast but not least, astrocytes normally form a network, able to influence at the same time a great number of different synapses ,33.Now, the most common brain cancers derive from glial cells, especially from astrocytes; these tumors are very aggressive and show a high mitotic index . In addiR. norvegicus embryos.Our research study aimed at contributing to the understanding of some dysfunctional alterations of astrocytes (which are thought to be responsible for the genesis of most brain tumors) through the isolation of astroglial cell lines from The general aim was to obtain an in vitro model, composed of various astrocyte cell lines , with anWe analyzed, with a proteomic approach, the A-FC6 cells, the most unstable of the previously obtained astrocyte clones , with thThe metalloproteinases Mmp3 and Mmp13, which are known to be involved in tumor invasion processes, since these proteases can degrade the extracellular matrix and also intracellular polymers, causing inflammation ,35. ThisChondroitin sulfate proteoglycan 4 (Cspg4), involved in the cell–substrate interaction of the endothelium and in the maturation of oligodendrocytes ,37;Phospholipase C-delta1 (Pcld1), involved in cell cycle control ;Poly (ADP-ribose) polymerase 3 (Parp3), involved in different transcriptional regulatory pathways in neuronal development .Thus, qualitative and quantitative proteomic analyses performed on primary astrocytes and A-FC6 cells suggested that the presence of the supernumerary genes in i(8q) does not result in their widespread increase in protein expression. In other words, from a proteomic point of view, it does not seem that there has been an appreciable upheaval, compared to primary astrocytes, in spite of the exclusive presence of some proteins , such asThe E3 ubiquitin protein ligase (Nedd4), which, according to what is reported in the literature, is involved in the growth and development of neurons ,41;Dynamin 2 (Dnm2), which participates in pre- and post-synaptic vesicular trafficking and endocytosis ,43;Von Willebrand factor (Vwa5a), a glycoprotein secreted by the endothelium and involved in some processes, such as platelet adhesion, development of the perivascular matrix and alteration of calcium signaling, glutamate absorption and the activity of some metalloproteinases ;Archain 1 (Arcn1), which directs the maturation of the cerebellum and the intracellular vesicular traffic and seems to play a direct role also in neurodegenerative diseases ;Inosine monophosphate dehydrogenase-2 (Impdh2), involved in the biosynthesis of GTP and in the support of cell proliferation ;Cellular retinoic acid binding protein 1 (Crabp1), which plays a key role in various phosphorylation pathways, cell proliferation and CNS regulation events ,48.Some specific upregulated proteins were also identified , such asIt is now widely recognized that extracellular vesicles constitute a form of universal and powerful communication that allows cells, from prokaryotes to the most complex eukaryotes, to exchange information. EVs indeed transport a variety of proteins, as well as coding and non-coding RNAs, lipids and metabolites, that can induce epigenetic adaptations in the surrounding receiving cells .In the mammalian brain, it is now clear that cell-to-cell communication mediated by EVs is of the highest importance for the regulation of differentiation of all the brain cell types, from neurons to astrocytes, endothelial cells, oligodendrocytes and microglia, and especially for the formation of synaptic contacts . EVs havAlthough EVs are fundamental vehicles for normal cell-to-cell communication in the brain, it is now clear that the ability to produce EVs can become a problem when the cells assume pathological phenotypes, as occurs in neurodegeneration or when they turn into cancer cells.The release of EVs is particularly intense in tumor cells, which use EVs and their content to escape immune surveillance and to create an environment prone to invasion .Herein, we report that EVs released from A-FC6 cells indeed contain proteins able to contribute to these processes. Among them, for example, are the following: (i) Cd59, a membrane complement regulatory protein that has been demonstrated to be overexpressed in most solid tumors, where it facilitates tumor cell escape from complement surveillance; (ii) integrins, a unique class of signaling molecules that are implicated in cancer progression as mechanotransducers and as key components of the cell migration machinery, the alterations of the expression patterns of which have been observed in most malignant tumors; (iii) matrix metalloproteinases, such as MMP3, which are able to modify the extracellular matrix, thus allowing tumor cell migration.In conclusion, our proteomic studies showed some functional proteomic modulation between the two cell types which is useful for the acquisition of malignant characteristics by the A-FC6 clone. However, although 100% of A-FC6 cells possess the cytogenetic marker i(8q), which increases the number of copies of the genes located on the q arm of chromosome 8, the proteins encoded by these genes, identified by Western blotting, are not consequently upregulated, suggesting a compensation mechanism at the level of protein expression due to post-transcriptional mechanisms.Most importantly, we found that EVs released from the transformed cells contain proteins that can facilitate escape from immune surveillance and that can allow remodeling of the extracellular matrix and cell invasion.


Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors.We included 8,073 people from NHANES (2017—March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis.P < 0.01 and P for trend <0.001.The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176–1.658), 1.499 (1.254–1.791), and 1.645 (1.360–1.989), respectively, with The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension. StudiesThe diagnosis of hypertension mainly includes (1) office blood pressure measurement (OBPM): a simple procedure but with a poor diagnosis effect for masked hypertension and white coat hypertension ; (2) 24 Artificial intelligence has gained popularity recently, and machine learning techniques, which are an important part of artificial intelligence, are extremely effective in identifying hypertension risk factors and predicting the risk of the disease. By integrating big data resources and using machine learning methods to construct morbidity risk prediction models, early identification of people at high risk of hypertension is possible. Through early intervention, the development of hypertension can be prevented and delayed, and thus, the risk of cardiovascular diseases can be significantly decreased –13. New Several studies around the world have constructed hypertension risk prediction models. These studies used different machine learning methods and incorporated different factors for prediction, and all showed high performance of the models –6. HowevIn this study, based on literature research and expert guidance, we incorporated more comprehensive information and extracted eligible subjects and their information from NHANES to develop a high-performance fusion model based on stacking to predict the risk of hypertension. In addition, serum ferritin (SF) was included in the best feature subset for the first time among many studies that predicted the risk of hypertension by machine learning methods; therefore, we further analyzed its association with hypertension and obtained a trend correlation between the risk of hypertension and SF levels.2.2.1.http://www.CDC.gov/nchs/nhanes/.The National Health and Nutrition Examination Survey (NHANES) is a population-based cross-sectional survey conducted by the National Center for Health Statistics (NCHS) to assess the health and nutrition status of adults and children in America –22. The n = 1,618) were excluded from the original dataset containing 15,561 participants. Then, the participants with a lot of missing information, such as missing more than 80% of any dimension of demographic, body measurement, laboratory test, or questionnaire were excluded . Finally, data on 8,073 participants were included in the study. A flowchart concerning participant selection is shown in Based on literature review and the guidance of experts from the Department of Cardiovascular Medicine, we extracted information on 120 items from 37 original datasets from the four dimensions of demographics, body measurements, laboratory tests, and questionnaires contained in the NHANES database , 24. Dat2.2.We classify patients with hypertension as hypertensive (0) if they have a history of taking medication for hypertension or a history of diagnosis. If there is no such condition, it can be divided by SBP/DBP .2.3.K nearest neighbors filling were used for categorical variables and continuous variables, respectively.After obtaining the original dataset, mode filling and Mode filling was achieved by replacing missing values with the plural  of the feature. We used it for filling the categorical variables because it is easy to implement and provides satisfactory results in most cases.K nearest neighbors filling is a similarity-based padding method. It fills the missing values by finding the nearest neighbors around missing values and using their average or weighted average. We used it to fill continuous variables because it accounts for the similarity between data points and can partly reduce bias.2.4.In this study, we first removed redundant features through LASSO regression and correlation analysis, which included features that were not significantly associated with the disease and one of the two features that were highly correlated (correlation coefficient ≥0.7), and then used recursive feature elimination (RFE) to determine the optimal feature subset . Since R(1)All features were used to construct the model;(2)The importance of each feature in the model was calculated;(3)The least important features were removed;(4)Steps 2 and 3 were repeated until the required number of features reached a predetermined value.Recursive Feature Elimination (RFE) is a commonly used feature selection method that selects features by recursively constructing models and gradually reducing the number of features , 25, 30.2.5.(1)A sample of a minority category was randomly selected.(2)K nearest neighbors of that sample were found.The (3)The nearest neighbor was randomly selected, and interpolation was performed between the neighbor and the sample to generate a new sample.(4)Steps 2 and 3 were repeated until the number of samples in the minority category reached a predetermined value.For processing data balance, we selected the most commonly used SMOTE (Synthetic Minority Over-sampling Technique), which increases the number of samples in a minority category by synthesizing new samples –33. The The SMOTE can be easily implemented and can effectively balance datasets with unbalanced categories, but it creates some synthetic samples that might not exist in the original dataset. Therefore, to avoid producing more synthetic samples and ensure that our study was conducted efficiently, we performed the data balancing step after determining the best feature subset.2.6.The prediction model constructed in this study is a multi-classification prediction, and to evaluate our model, five metrics were used: ACC (accuracy), precision, recall, F1 value, and AUC.Since this study is a triple classification task and considering the unbalance samples, the Macro Average rule was used to weight these evaluation criteria. If the patients with hypertension are recorded as True (1), the rest are False (0) and ACC1, P1, and R1 are calculated; the people with high blood pressure are recorded as True (1), the rest are False (0) and then calculate ACC2, P2, and R2; if normal people are recorded as True (1), the rest are False (0) and ACC3, P3, and R3 are calculated. From this, the corresponding F1 values can also be calculated separately, a final value can be obtained by the weighted average of the corresponding indexes of each group separately, which can be used as the evaluation index of this research model.In order to evaluate the model as more objective and accurate, 10-fold cross-validation was carried out for the final prediction model, that is, all samples in the data set were randomly divided into 10 mutually exclusive subsets with similar sizes and approximately the same number of events, and during each training round, 9 subsets were selected in turn to form the training set and the remaining subsets formed the test set. The model needs to run 10 times with different training and test sets, respectively, and the final result is the average of the 10 test results.2.7.After determining the optimal feature subset, to avoid the noise caused by filling in the missing values, we only retain the complete part of the optimal feature subset in the original data for the construction of the prediction model . We used2.8.P < 0.05 was considered statistically significant). Processes such as feature engineering, data balancing and prediction model construction were done in python , while TensorFlow 2.11.0; scikit-learn 1.1.3; keras 2.11.0; pandas1.5.2; numpy, etc. Package installation is complete.Statistical analysis used in this study was completed through SPSS 23 and R 4.1.3. For continuous variables, normality was first tested through the Kolmogorov-Smirnov test, means and standard deviations were used if they followed a normal distribution (ANOVA was used to compare continuous variables between groups); if they did not follow a normal distribution, the median (quartiles) were used (non-parametric tests were used to compare continuous variables between groups). For categorical variables, counts and percentages were used , and all statistical tests were two-sided  years old.According to the classification criteria of this study: 3,527 (43.7%) were hypertensive, 892 (11.0%) were pre-hypertensive; 3,654 (45.3%) were normal blood pressure. The mean blood pressure of hypertensive patients was 134 (121–147) mmHg/82 (80–88) mmHg; the mean blood pressure of pre-hypertensives was 129 (122–133) mmHg/79 (71–85) mmHg; the mean blood pressure of normotensives was 113 (106–121) mmHg/70 (65–75) mmHg. More basic information such as BMI, waist circumference, educational status, marital status, physical activity, and smoking status is shown in 3.2.After LASSO regression analysis, we excluded 12 redundant characteristics: platelet count, total bilirubin, Cotinine, serum cotinine, number of ready-to-eat foods in the last month, number of frozen foods/pizza in the last month, whether or not had asthma, whether or not exercising on the advice of their doctor, whether or not had seen a psychiatrist in the last year, dairy consumption in the last 30 days, weight loss through diet pills/surgery, whether or not have hepatitis B, whether or not had tried to quit smoking. After correlation analysis, we excluded 8 more characteristics: hip circumference, Red cell distribution width, Aspartate Aminotransferase (AST), glucose (frozen serum), Osmotic pressure, total cholesterol (frozen serum), triglycerides (frozen serum), and urinary albumin. After a preliminary model trial, we found that among these thirteen ways the four tree-based classification models, i.e., XGBoost, Extra Tree, Random Forest, and LightGBM, performed better . TherefoEach of these four models has its corresponding optimal feature subset . Therefo3.3.n = 5973) with all 10 variables complete in the original data for model prediction and evaluation. Then, we selected RF, XGB, LGBM, and ET with better performance to construct the prediction models. We plotted the ROC curves with "1-specificity" as horizontal coordinate and "sensitivity" as vertical coordinate, and all four classifiers showed good performance with AUC above 0.90 , and the mean levels of the three groups are shown in Since data of SF did not obey normal distribution, we performed the Kruskal-Wallis test and found that the difference in serum ferritin between the three groups was statistically significant  was taken as reference, and model 0 was crude. Model 1 controlled for demographic information ; model 2 controlled for anemia and inflammation-related indicators ; model 3 controlled for BMI, waist, some potentially confounding medical history  and related metabolic indicators ; model 4 controlled for smoking, alcohol consumption, diet, sleep, exercise and other lifestyle habits and depression scores; model 5 controlled for factors with significant differences above-mentioned.P < 0.01. Therefore, we carried out trend analysis for the five models with the help of clm function in R, and the P for trend value was <0.001, indicating a trend correlation between the risk of hypertension prevalence and SF levels. The results of the above five models are shown in Since all models above showed that compared with the lowest level group (Q1), the other three groups  had higher risk, and the OR values with their 95% CI were greater than 1, and the OR values were increasing sequentially with 4.Our study proposes a high-performance and convenient model for predicting the risk of hypertension, which can be used as an automated diagnostic aid in a hospital environment in conjunction with EMR data for real-time prediction.In the preliminary screening stage of the model, we used 13 methods to construct the prediction model in parallel and used ACC, AUC and F1 as the measures, and found that four decision tree type methods, RF, XGB, LGBM and ET, performed better. We consider that it may be because the decision tree type methods have advantages in dealing with high-dimensional data and nonlinear relationships, and can better capture patterns in data features, which are more suitable for the data features and prediction task requirements of this study. In addition, the fusion model constructed based on the stacking strategy also has better stability and can be more reliably applied in clinical practice.The single measurement of SBP and DBP fluctuates rapidly under the influence of various factors such as mood, sleep, exercise and time. However, our proposed prediction model incorporates laboratory tests with relatively stable changes. For example, HbA1C is used to assess the average level of blood glucose levels over the past 2 to 3 months; creatinine ratio is used to reflect changes in kidney function and muscle metabolism, it always changes little in people with normal kidney function, but changes within a month in people with kidney disease; serum creatinine is used to reflect changes in filtration function of the kidney, it only changes dramatically in days in patients with acute kidney injury and significantly in months or years in patients with chronic kidney disease; fluctuations in SF are generally not significant, usually taking weeks or months, and for the treatment of anemic patients, changes in this index also take 1 to 3 months, unless the patient experiences acute inflammation, which can change dramatically in a short period of time. In a short, such a predictive model is more stable compared to office blood pressure measurements. The included laboratory data are valid for at least 1 month. Also, multiple inputs can be obtained from HIS , it is more cost-effective than ambulatory blood pressure. We considered that it could be used as an early warning signal to reduce admission rate, readmission rate and their expenses, avoiding progression to more serious disease , 38. It In addition, hypertension is a multi-factorial disease, and many factors such as age, BMI, smoking, alcohol consumption, and excessive salt consumption are widely recognized by clinical experts as major influencing factors. Therefore, most studies on hypertension risk prediction have focused on these factors. Our study, based on data from NHANES 2017 to March 2020, incorporated more comprehensive information. We considered the optimal subset of features selected, and with the exception of serum ferritin, the other 9 features have been extensively documented to correlate with hypertension, especially factors such as age and BMI have been widely accepted as influencing factors in the clinic. SF has been less studied in the field of hypertension, so we decided to further explore the relationship between SF and hypertension.SF is a recognized biomarker used to evaluate the status of iron (iron deficiency or iron loading) , 44 and The SF levels can increase significantly due to inflammation or the occurrence of certain diseases  and play an important role in energy metabolism disorders . For exaBy searching several databases, we found that very few studies investigated the relationship between SF and hypertension. Considering that high levels of SF are a characteristic of insulin resistance-associated hepatic iron overload syndrome (IRHIO), Piperno et al.  divided These four studies either confirmed significant differences in SF between hypertensive and control groups with only a small number of participants, or found that baseline SF levels were significantly higher in hypertensive patients than in normal participants through follow-up, but did not fully account for more relevant confounding factors. We comprehensively considered all the factors that may affect hypertension and SF, and included them as co-variables, and objectively investigated the correlation between SF and hypertension.Although SF is an inexpensive and widely used biomarker, it has not been used to extensively study its association with hypertension or its complications related to cardiovascular disease and is less commonly included in routine physical examinations.Of course, our study has several shortcomings. (1) This was a cross-sectional study that did not take into account the long-term development and progression of the disease. (2) Our study is only based on NHANES database, and its representation is limited to the US population. (3) The prediction model constructed in this study only predicted the end event, without further study on disease progression, such as how long it will take to develop the disease. (4) In this study, the relationship between hypertension and SF needs to be further explored on the temporal relationship.Although the results of this study are limited to the US population, we believe that the methodology proposed in this study is transferable, and we are currently conducting similar studies on Chinese populations to further validate the findings of this paper. In future studies, we will verify the usability of the prediction model constructed in this study on a larger data set based on real hospital data or in cooperation with multiple medical institutions by means of multi-center cooperation, and in this process, we will also pay more attention to sample balance. At the same time, we hope to further confirm the relationship between SF and hypertension using multiple datasets from different sources as well as explore the causal relationship between the two through cohort studies and other means.5.In this study, we propose a cost-effective hypertension risk prediction model based on the stacking strategy that integrates demographic, physical measurements, and physiological and biochemical indicators, etc. to incorporate more comprehensive information. Further statistical analysis of the SF suggested by the optimal subset of characteristics was also performed, and showed that serum ferritin level was trending correlated with the risk of hypertension. In the future, we aim to collect more targeted data and construct more robust predictive models to assist clinicians in diagnosis and increase patient awareness of the disease.