Pramlintide an Adjunct to Insulin Therapy: Challenges and Recent Progress in Delivery.

Dysregulation of various glucoregulatory hormones lead to failure of insulin monotherapy in patients with diabetes mellitus due to various reasons, including severe hypoglycemia, glycemic hypervariability, and an increased risk of microvascular complications. However, pramlintide as an adjunct to insulin therapy enhances glucagon suppression and thereby offers improved glycemic control. Clinical studies have shown that pramlintide improves glycemic control, reduces postprandial glucose excursions, and promotes weight loss in patients with type 1 and type 2 diabetes. Although clinical benefits of pramlintide are well reported, there still exists a high patient resistance for the therapy, as separate injections for pramlintide and insulin must be administered. Although marketed insulin formulations generally demonstrate a peak action in 60-90 minutes, pramlintide elicits its peak concentration at around 20-30 minutes after administration. Thus, owing to the significant differences in pharmacokinetics of exogenously administered pramlintide and insulin, the therapy fails to elicit its action otherwise produced by the endogenous hormones. Hence, strategies such as delaying the release of pramlintide by using inorganic polymers like silica, synthetic polymers like polycaprolactone, and lipids have been employed. Also, approaches like noncovalent conjugation, polyelectrolyte complexation, and use of amphiphilic excipients for codelivery of insulin and pramlintide have been explored to address the issues with pramlintide delivery and improve patient adherence to the therapy. This approach may usher in a new era of diabetes management, offering patients multiple options to tailor their treatment and improve their quality of life. SIGNIFICANCE STATEMENT: To our knowledge, this is the first report that summarizes various challenges in insulin and pramlintide codelivery and strategies to overcome them. The paper also provides deeper insights into various novel formulation strategies for pramlintide that could further broaden the reader's understanding of peptide codelivery.

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across.

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

Amendments in surgical pathology reports: An 8-year institutional experience.

Surgical pathology reports may undergo revisions broadly categorized as addenda (supplementary information) or amendments (changes to finalized reports). Amendments indicate potential flaws in the diagnostic process and serve as important indicators of vulnerabilities in the histopathology workflow. This study analyzed the frequency and distribution of amendments in surgical pathology reports over 8 years to identify patterns highlighting opportunities for improvement. Surgical biopsies, excisions, and resections were included; cytology and molecular tests were excluded. Amended reports were categorized using previously used taxonomy documented in literature. Defects were classified as misinterpretations, misidentifications, defective specimens, or defective reports. Of 101,355 reports, 155 (0.15 %) were signed out with amendments. The amendment rate was approximately 1-2 cases per 1000 reports annually. Misinterpretations accounted for the majority (52 %) of amended reports, with undercalls (62 %) and overcalls (27 %) being predominant subtypes. Tumor staging was amended in 57 (37 %) cases, with 30 being upstaged and 11 downstaged clinically. The highest number of misinterpretation defects occurred in head and neck (36 %) and breast (21 %) specimens. Misinterpretation defects were present in 53 % of malignant cases versus 42 % of benign cases. In 18 cases, there were significant changes in pathological diagnosis (14 major and 4 minor). A standard taxonomy categorizing report defects is crucial for measuring and improving quality control. Accurate pathology reporting impacts patient care and guides workflow improvements. This taxonomy enables us to track variations and deficiencies in our pathology reporting processes in a reproducible way across the department.

SNOT-22 scores after 6 months of aspirin therapy are predictive of long-term quality of life in AERD.

Background: Aspirin exacerbated respiratory disease (AERD) is an inflammatory condition that consists of eosinophilic asthma, chronic rhinosinusitis with nasal polyps, and respiratory reactions to cyclooxygenase-1 inhibitors. Aspirin therapy after aspirin desensitization (ATAD) is the most extensively studied treatment paradigm for AERD. Objective: The objective was to identify which time point of ATAD was most predictive of long-term outcomes as measured by the 22-item Sino-Nasal Outcome Test (SNOT-22). Methods: A retrospective chart review was conducted of patients at a single institution who underwent endoscopic sinus surgery, followed by ATAD, and had remained on ATAD for 2 consecutive years. SNOT-22 scores were recorded at predesensitization as well as at the 3-, 6-, 12-, and 24-month postdesensitization time points. The patients were separated into two cohorts at each of the data collection time points based on whether their SNOT-22 scores were < 20 (responders) or ≥ 20 (nonresponders). Responder status was compared between each time point and at 24-month postdesensitization. The odds ratios (OR) were then calculated between the two groups at each of the following time points: postsurgery/predesensitization, and 3-, 6-, and 12-month postdesensitization. Results: There were 70 patients who met the inclusion criteria of having 24-month postdesensitization SNOT-22 scores available. Responder status at 6 months after surgery had the most predictive OR 16.5 (95% confidence interval, 3.71-73.44) for long-term outcomes at 24 months. Conclusion: The SNOT-22 scores after 6 months of ATAD showed the greatest predictive value for long-term quality-of-life outcomes and, therefore, poor 6-month SNOT-22 scores could serve as a basis for consideration of alternative therapies.

Heartfulness Cleaning and Meditation: Short-Term Effects on Attention and Affect.

Heartfulness meditation (HM) trains the practitioner's attention as they progress towards reaching a super-conscious state. The process is supported by guided "Heartfulness cleaning," which helps clear the mind. This study aimed to examine the short-term effects of HM on affect and cognition and determine whether performing Heartfulness cleaning beforehand influenced the meditation outcome. Forty-eight experienced meditators (age range: 19-71 years and a male-to-female ratio: 27:21) were randomly assigned to 3 sessions: (i) HM, (ii) Heartfulness meditation preceded by cleaning, and (iii) quiet rest as a control. Mood state and emotional well-being were assessed before and after each intervention using established scales such as the Brief Mood Introspection Scale, Global Vigor and Affect Scale, Spielberger's State-Trait Anxiety Inventory, and the Digit Letter Substitution Test. After engaging in both HM and Heartfulness cleaning meditation (HCM) practices, there was a noticeable increase in feelings of pleasantness (7.3%, 7.0%, respectively) and positivity (7.5%, 7.8%, respectively), accompanied by a decrease in negative affect (14.4%, 16.5%, respectively). Additionally, HM and HCM increased in the net and total scores on a substitution test designed to measure associative learning. In contrast, there were no changes observed after 30 minutes of non-meditation. In summary, the findings of this study provide support for the positive impact of Heartfulness meditation and Heartfulness cleaning meditation on emotions, as well as their ability to enhance performance in tasks involving complex attention and associative learning. It should be noted that preceding Heartfulness meditation with 5 minutes of Heartfulness cleaning did not significantly alter the overall outcome of the meditation practice.

Dengue outbreak update in India: 2022.

Dengue is an emerging illness in India, where it is endemic in some areas and sometimes causes yearly epidemics. Each dengue outbreak starts with high death and morbidity, which has a significant socioeconomic impact. As of September 30, 2022, India had 63,280 dengue cases, according to information provided by the National Centre for Vector Borne Diseases Control. North India is most severely impacted by each outbreak. In Uttar Pradesh, the state with the most population in India, there have been 2060 confirmed cases of dengue and 1 mortality till September 2022 reported. Patients are being reported from semi-urban, rural, and urban areas. It is essential to properly monitor disease cases through disease surveillance in order to ensure prompt case management if dengue outbreak control is to be achieved. An efficient diagnostic approach for early diagnosis is urgently required to reduce the severity of the sickness, the length of the hospital stay, and clinical consequences.

A comparative study on comprehensive nutritional profiling of indigenous non-bio-fortified and bio-fortified varieties and bio-fortified hybrids of pearl millets.

Seven indigenous pearl millet varieties, including non-bio-fortified (HC-10 & HC-20) and bio-fortified (Dhanashakti) and bio-fortified hybrids, viz., AHB-1200, HHB-299, HHB-311, and RHB-233, were studied in the present work. There was not any significant difference observed in the crucial anti-nutrients content, i.e., phytate (24.88-32.56 mg/g), tannin (3.07-4.35 mg/g), and oxalate (0.33-0.43 mg/g). Phytochemical content and antioxidant activity showed significantly high (p < 0.05) TPC and FRAP, TFC, and DPPH radical scavenging activity in the HHB 299 and Dhanashakti, respectively. Quantitative analysis of polyphenols by HPLC (first report on these varieties) revealed that HHB-299 has the highest amount of gallic acid. Fatty acid profiling by GC-FID showed that Dhanashakti, AHB-1200, and HHB-299 have rich monounsaturated fatty acid (MUFA) and polyunsaturated fatty acids (PUFA). Mineral analysis by ICP-OES showed high iron (87.79 and 84.26 mg/kg) and zinc (55.05 and 52.43 mg/kg) content in the HHB-311 and Dhanashakti, respectively. Results of the present study would help facilitate the formulation of various processed functional food products (RTC/RTE) that are currently not reported/unavailable. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-022-05452-x.

A novel inhibitor L755507 efficiently blocks c-Myc-MAX heterodimerization and induces apoptosis in cancer cells.

c-Myc is a transcription factor that plays a crucial role in cellular homeostasis, and its deregulation is associated with highly aggressive and chemotherapy-resistant cancers. After binding with partner MAX, the c-Myc-MAX heterodimer regulates the expression of several genes, leading to an oncogenic phenotype. Although considered a crucial therapeutic target, no clinically approved c-Myc-targeted therapy has yet been discovered. Here, we report the discovery via computer-aided drug discovery of a small molecule, L755507, which functions as a c-Myc inhibitor to efficiently restrict the growth of diverse Myc-expressing cells with low micromolar IC50 values. L755507 successfully disrupts the c-Myc-MAX heterodimer, resulting in decreased expression of c-Myc target genes. Spectroscopic and computational experiments demonstrated that L755507 binds to the c-Myc peptide and thereby stabilizes the helix-loop-helix conformation of the c-Myc transcription factor. Taken together, this study suggests that L755507 effectively inhibits the c-Myc-MAX heterodimerization and may be used for further optimization to develop a c-Myc-targeted antineoplastic drug.

Leveraging AI for advanced analytics to forecast altered tourism industry parameters: A COVID-19 motivated study.

COVID-19 pandemic has given a sudden shock to economy indices worldwide and especially to the tourism sector, which is already very sensitive to such crises as natural calamities, terrorist activities, virus outbreaks and unwanted conditions. The economic implications for a reduction in tourism demand, and the need to analyse post-COVID-19 tourism motivates our research. This study aims to forecast the future trends for foreign tourist arrivals and foreign exchange earnings for India and to formulate a model to predict the future trends based on the COVID-19 parameters, vaccinations and stringency index (Government travelling guidelines). In the study, we have developed artificial intelligence models (random forest, linear regression) using the stacked based ensemble learning method for the development of base models and meta models for the study of COVID-19 and its effect on the tourism industry. The architecture of a stacking model consists of two or more base models, often referred to as level-0 models, and a meta-model that combines the predictions of the base models, and is referred to as a level-1 model (Smyth & Wolpert, 1999). The results show that the projected losses require quick action on developing new practices to sustain and complement the resilience of tourism per se.

Conformational dynamics of 13 amino acids long NSP11 of SARS-CoV-2 under membrane mimetics and different solvent conditions.

The intrinsically disordered proteins/regions (IDPs/IDPRs) are known to be responsible for multiple cellular processes and are associated with many chronic diseases. In viruses, the existence of a disordered proteome is also proven and is related to its conformational dynamics inside the host. The SARS-CoV-2 has a large proteome, in which, structure and functions of all proteins are not known yet, along with non-structural protein 11 (nsp11). In this study, we have performed extensive experimentation on nsp11. Our results based on the CD spectroscopy gives characteristic disordered spectrum for IDPs. Further, we investigated the conformational behavior of nsp11 in the presence of membrane mimetic environment, α-helix inducer, and natural osmolyte. In the presence of negatively charged and neutral liposomes, nsp11 remains disordered. However, with SDS micelle, it adopted an α-helical conformation, suggesting the helical propensity of nsp11. Finally, we again confirmed the IDP behavior of nsp11 using MD simulations. In future, this conformational dynamic study could help to clarify its functional importance in SARS-CoV-2 infection.

Effectiveness of endoscopic sinus surgery and aspirin therapy in the management of aspirin-exacerbated respiratory disease.

Background: Aspirin therapy and/or type 2 (T2) biologics are used in the management of aspirin-exacerbated respiratory disease (AERD). Objective: To identify the number of patients with AERD who tolerated aspirin therapy, yet due to persistent symptoms, incorporated T2 biologic management. Methods: A retrospective review was performed between July 2016 and June 2019. Patients with AERD and who underwent endoscopic sinus surgery (ESS), aspirin desensitization (AD), and at least 6 months of aspirin therapy (ATAD) after AD, and who remained biologic-naive up through this timepoint were included in the study. Introduction of a T2 biologic while on ATAD was the primary outcome. The secondary outcome was a change in a validated patient-reported outcome measure for chronic rhinosinusitis score between the postoperative predesensitization timepoint, and the 6-month postdesensitization timepoint, presented as means and compared by using the Student's t-test. Results: A total of 103 patients met inclusion criteria. Two patients (1.9%) ultimately supplemented ATAD with a T2 biologic. The mean outcomes measure test score after 6 months of ATAD for patients who received biologics was 40.5 versus 15 in those who did not receive biologics (p = 0.02). The mean differences between the postoperative predesensitization test score and the 6-month postdesensitization test score for patients who went on to receive biologics was an increase of 13 versus a decrease of 10 for those patients who did not receive biologics (p = 0.12). Conclusion: ESS, coupled with AD and ATAD, was successful in the long-term management of the majority of the patients with AERD, which rarely required the incorporation of T2 biologics. Patient questionnaires, such as outcomes measure test score, may identify aspirin therapy failures and help guide the practitioner in deciding when to introduce T2 biologics into the patient's treatment regimen.

Zika virus NS4A N-Terminal region (1-48) acts as a cofactor for inducing NTPase activity of NS3 helicase but not NS3 protease.

Among Flaviviridae, in West Nile virus (WNV) and Hepatitis C virus (HCV), the non-structural protein NS4A modulates the NTPase activity of viral helicases during nucleic acid unwinding through its N-terminal disordered residues (1-50). In HCV, the acidic NS4A also serves as a cofactor for regulating the NS3 protease activity. However, in case of Zika virus (ZIKV), the role of NS4A and its impact on activities of NS3 helicase and protease is not known. In order to elucidate the role of NS4A, we checked the NTPase activity of NS3 helicase and protease activity of NS3 protease in presence of NS4A N-terminal region (residues 1-48) peptide. Our enzyme kinetics results together with binding experiment clearly demonstrate that NS3 helicase in presence of NS4A peptide increased the rate of ATP hydrolysis whereas the protease activity of NS3 protease was not affected. Therefore, like WNV and HCV, our results establish a role of ZIKV NS4A being a cofactor for modulating the NTPase activity of ZIKV NS3 helicase.

Chronic Granulomatous Disease Due to Neutrophil Cytosolic Factor (NCF2) Gene Mutations in Three Unrelated Families.

Chronic granulomatous disease (CGD) is an inheritable and genetically heterogeneous disease resulting from mutations in different subcomponents of the NADPH oxidase system. Mutations in the NCF2 gene account for <5% of all cases of CGD. We analyzed the clinical and laboratory findings of CGD with mutations in the NCF2 gene from amongst our cohort of CGD patients. A homozygous mutation (c.835_836delAC, p.T279fsX294), a deletion in NCF2 gene was found in two cases. In the third case, two heterozygous mutations were detected, IVS13-2A>T on one allele and c.1099C>T (p.) on the other allele. The mother of this child was a carrier for the IVS13-2A>T mutation. All three cases had colitis, and it was the initial symptom in two patients. One of the patients also developed a lung abscess due to Nocardia cyriacigeorgica.

A Young Child with Eosinophilia, Rash, and Multisystem Illness: Drug Rash, Eosinophilia, and Systemic Symptoms Syndrome After Receipt of Fluoxetine.

Drug rash, eosinophilia, and systemic symptoms (DRESS) syndrome is a severe systemic hypersensitivity reaction that usually occurs within 6 weeks of exposure to the offending drug. Diagnosis is usually straightforward in patients with pyrexia, skin rash, hepatitis, and eosinophilia with a preceding history of exposure to agents often associated with DRESS syndrome, such as aromatic anticonvulsants and sulfa drugs, but diagnosis of DRESS may still be a challenge. We report a 4-year-old child with probable DRESS syndrome complicated by multiple hematologic complications that developed 1 month after exposure to fluoxetine, a drug not known to be associated with such severe reactions.

Ayurvedic doshas as predictors of sleep quality.

BACKGROUND: The 3 Ayurvedic constitutional types or Doshas--vata, pitta, and kapha--are responsible for homeostasis and health. The doshas determine various functions, including sleep. According to the Ayurvedic texts, sleep is caused by increased kapha and insomnia by increased vata or pitta, which may follow physical or mental exertion, or disease. The present study was carried out to determine whether this relationship could be found using contemporary standardized questionnaires. MATERIAL/METHODS: In this cross-sectional single-group study, 995 persons participated (646 males; group average age ±S.D., 49.1±15.2 years). Participants were attending a 1-week residential yoga program in northern India. Participants were assessed for dosha scores using a Tridosha questionnaire and the quality of sleep in the preceding week was self-rated using a sleep rating questionnaire. RESULTS: Multiple linear regression analyses were used to determine if each dosha acted as a predictor of quality and quantity of sleep. Vata scores significantly predicted the time taken to fall asleep [p<0.01], and the feeling of being rested in the morning [p<0.001]; with higher vata scores being associated with a longer time to fall asleep and a lesser feeling of being rested in the morning. Kapha scores significantly predicted day-time somnolence [p<0.05] and the duration of day-time naps in minutes [p<0.05], with higher kapha scores being associated with longer day-time naps. CONCLUSIONS: The results suggest that the doshas can influence the quality and quantity of sleep.

Safer and greener chemicals for the aquatic ecosystem: Chemometric modeling of the prolonged and chronic aquatic toxicity of chemicals on Oryzias latipes.

In the modern era, chemicals and their products have been used everywhere like agriculture, healthcare, food, cosmetics, pharmaceuticals, household products, clothing industry, etc. These chemicals find their way to reach the aquatic ecosystem (directly/indirectly) and cause severe chronic and prolonged toxic effects to aquatic species which is also then translated to human beings. Prolonged and chronic toxicity data of many chemicals that are used daily is not available due to high experimentation testing costs, time investment, and the requirement of a large number of animal sacrifices. Thus, in silico approaches (e.g., QSAR (quantitative structure-activity relationship)) are the best alternative for chronic and prolonged toxicity predictions. The present work offers multi-endpoint (five endpoints: chronic_LOEC, prolonged_14D_LC50, prolonged_14D_NOEC, prolonged_21D_LC50, prolonged_21D_NOEC) QSAR models for addressing the prolonged and chronic aquatic toxicity of chemicals toward fish (O. latipes). The statistical results (R2 =0.738-0.869, QLOO2 =0.712-0.831, Q(F1)2 =0.618-0.731) of the developed models show that they were robust, reliable, reproducible, accurate, and predictive. Some of the features that are responsible for prolonged and chronic toxicity of chemicals towards O. latipes are as follows: the presence of substituted benzene, hydrophobicity, unsaturation, electronegativity, the presence of long-chain fragments, the presence of a greater number of atoms at conjugation, and the presence of halogen atoms. On the other hand, hydrophilicity and graph density descriptors retard the aquatic chronic and prolonged toxicity of chemicals toward O. latipes. The PPDB (pesticide properties database) and experimental and investigational classes of drugs from the DrugBank database were also screened using the developed model. Thus, these multi-endpoint models will be helpful for data-gap filling and provide a broad range of applicability. Therefore, this research will aid in the in silico QSAR (quantitative structure-activity relationship) prediction (non-animal testing) of the prolonged and chronic toxicity of untested and new toxic chemicals/drugs/pesticides, design and development of eco-friendly, novel, and safer chemicals, and help to protect the aquatic ecosystem from exposure to toxic and hazardous chemicals.

The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions.

Direct or indirect consumption of pesticides and their related products by humans and other living organisms without safe dosing may pose a health risk. The risk may arise after a short/long time which depends on the nature and amount of chemicals consumed. Therefore, the maximum acceptable daily intake of chemicals must be calculated to prevent these risks. In the present work, regression-based quantitative structure-activity relationship (QSAR) models were developed using 39 pesticides with maximum acceptable daily intake (MADI) for humans as the endpoint. From the statistical results (R2 = 0.674-0.712, QLOO2 = 0.553-0.580, Q(F1)2 = 0.544-0.611, and Q(F2)2 = 0.531-0.599), it can be inferred that the developed models were robust, reliable, reproducible, accurate, and predictive. Intelligent Consensus Prediction (ICP) was employed to improve the external predictivity (Q(F1)2 =0.579-0.657 and Q(F2)2 = 0.563-0.647) of the models. Some of the chemical markers responsible for toxicity enhancement are the presence of unsaturated bonds, lipophilicity, presence of C< (double bond-single bond-single bonded carbon), and the presence of sulphur and phosphate bonds at the topological distances 1 and 6, while the presence of hydrophilic groups and short chain fragments reduces the toxicity. The Pesticide Properties Database (PPDB) (1694 pesticides) was also screened with the developed models. Hence, this research work will be helpful for the toxicity assessment of pesticides before their synthesis, the development of eco-friendly and safer pesticides, and data-gap filling reducing the time, cost, and animal experimentation. Thus, this study might hold promise for future potential MADI assessment of pesticides and provide a meaningful contribution to the field of risk assessment.

First report on regression-based QSAR addressing pesticide dissipation half-life in plants: A step towards sustainable public health.

The excessive use of pesticides (an important group of chemicals) in the agricultural as well as public sectors raises a health concern. Pesticides affect humans and other living organisms via the food chain. Therefore, it is very necessary to calculate the dissipation half-life of pesticides in plants. Experimental prediction of pesticide dissipation half-lives requires complex environmental conditions, high cost, and a long time. Thus, in-silico half-life predictions are suitable and the best alternative. Herein, a total of six PLS (partial least squares) models namely, M1 (overall), M2 (fruit), M3 (plant interior), M4 (leaf), M5 (plant surface), and M6 (whole plant) alongside two MLR (multiple linear regression) models i.e. M7 (fruit surface) and model M8 (straw) were generated using dissipation half-lives (log10(T1/2)) of pesticides in plants and their different parts. Models were constructed in strict accordance with the guidelines outlined by the Organization for Economic Co-operation and Development (OECD) and extensively validated using globally accepted validation metrics (determination coefficient (R2) = 0.610-0.795, leave-one-out (LOO) cross-validated correlation coefficient (Q2LOO) = 0.520-0.660, MAE-FITTED TRAIN (mean absolute error fitted train) = 0.119-0.148, MAE-LOOTRAIN = 0.132-0.177, predictive R2 or Q2F1 = 0.538-0.567, Q2F2 = 0.500-0.565, MAETEST = 0.122-0.232), confirming their accuracy, reliability, predictivity, and robustness. Lipophilicity, the presence of a cyclomatic ring, suphur, aromatic amine fragments, and chlorine atom fragments are responsible (+ve contribution) for high dissipation half-lives of pesticides in plants. In contrast, hydrophilicity, pyrazine fragments, and rotatable bonds reduce (-ve negative contribution) the dissipation half-lives of pesticides in plants. To address the real-world applicability, the models were employed to screen the PPDB (Pesticide Properties Database) database, which revealed the top 10 pesticides with the highest log(T1/2) in the whole plant and respective parts of the plant body. The present work will aid in developing safer and novel pesticides, regulatory risk assessment, various risk assessments for the sustenance of public health, screening of databases, and data-gap filling.