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    In Python, all exceptions must be instances of a class that derives from BaseException . In a try statement with an except clause that mentions a particular class, that clause also handles any exception classes derived from that class (but not exception classes from which it is derived). Two exception classes that are not related via subclassing are never equivalent, even if they have the same name.

    The built-in exceptions listed below can be generated by the interpreter or built-in functions. Except where mentioned, they have an “associated value” indicating the detailed cause of the error. This may be a string or a tuple of several items of information (e.g., an error code and a string explaining the code). The associated value is usually passed as arguments to the exception class’s constructor.

    User code can raise built-in exceptions. This can be used to test an exception handler or to report an error condition “just like” the situation in which the interpreter raises the same exception; but beware that there is nothing to prevent user code from raising an inappropriate error.

    The built-in exception classes can be subclassed to define new exceptions; programmers are encouraged to derive new exceptions from the Exception class or one of its subclasses, and not from BaseException . More information on defining exceptions is available in the Python Tutorial under User-defined Exceptions .

    When raising (or re-raising) an exception in an except or finally clause __context__ is automatically set to the last exception caught; if the new exception is not handled the traceback that is eventually displayed will include the originating exception(s) and the final exception.

    When raising a new exception (rather than using a bare raise to re-raise the exception currently being handled), the implicit exception context can be supplemented with an explicit cause by using from with raise :

    The expression following from must be an exception or None . It will be set as __cause__ on the raised exception. Setting __cause__ also implicitly sets the __suppress_context__ attribute to True , so that using raise new_exc from None effectively replaces the old exception with the new one for display purposes (e.g. converting KeyError to Sperry Saltwater Wedge Tide Wool AXtl0N3cDf
    , while leaving the old exception available in __context__ for introspection when debugging.

    QUESTIONS TO CONSIDER

    Does the rise in creatinine indicate deterioration of kidney function?

    How do you explain the discrepancy between the 2 markers of kidney function?

    What is the nature of the fluid collection at the right upper pole?

    What test could be used to determine the nature of the fluid collection?

    Serum creatinine (molecular weight 113 Da) is derived from muscle metabolism and strongly influenced by muscle mass, diet, age, and sex. By contrast, cystatin C, a low molecular weight protein (13.3 kDa), is produced by all nucleated cells at a constant rate, making it particularly useful in children and patients with body mass outside the reference interval ( Touch Ups Melanie Sandal Womens nYtlZX
    ). Therefore, low creatinine values in the presence of cystatin C concentrations at or above the reference interval are suggestive of muscle wasting, while the opposite may indicate very high muscle mass or excessive intake of cooked meat, fish, or creatine supplements (e.g., body builders) ( 4 ). Cystatin C is not an acute-phase protein. However, cystatin C production is influenced by disturbances in thyroid metabolism. Untreated hypothyroidism leads to decreased cystatin C concentrations, while the opposite is observed in hyperthyroidism ( 3 ). High-dose corticosteroids stimulate cystatin C production ( 3 ). Neither thyroid disease ( 5 ) nor corticosteroid therapy (Bökenkamp, unpublished data) affect creatinine metabolism.

    There are large differences in the volume of distribution between creatinine and cystatin C. While creatinine equilibrates in total body water, cystatin C is restricted to the extracellular space. This is reflected in more rapid changes in serum cystatin C as compared to creatinine during acute changes in kidney function ( New Balance Vazee Rush V3 Athletic Sneaker HN5z3gcvb
    ).

    Both creatinine and cystatin C are eliminated almost exclusively via glomerular filtration. However, creatinine also undergoes renal tubular secretion. This process increases with declining glomerular filtration leading to the so-called “creatinine-blind range” between 50 and 90 mL/min/1.73 m 2 , in which moderately decreased glomerular filtration is concealed by tubular secretion ( 7 ). This is particularly worrisome in patients with low creatinine production ( 4 ). Although cystatin C is predominantly excreted via the kidney, there is some hepatic elimination accounting for about 5% at normal GFR ( 6 ). The relative contribution of extrarenal elimination increases with declining GFR and underlies the observation that cystatin C concentrations do not exceed 10 mg/L even in anuric patients ( 8 ). Thus, compared to creatinine, the rise in cystatin C is stronger with mildly to moderately impaired renal function (chronic kidney disease stage 2–3), whereas the opposite applies to severe and end-stage renal failure (chronic kidney disease stage 4–5).

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    Hundreds of programming languages supported #

    In Visual Studio Code, we have support for almost every major programming language. Several ship in the box, for example, JavaScript, TypeScript, CSS, and HTML but more rich language extensions can be found in the VS Code Marketplace .

    Here are eight of the most popular language extensions:

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    C# for Visual Studio Code (powered by OmniSharp).
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    Rich Go language support for Visual Studio Code
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    Popular VS Code extensions for Java development.
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    Provides Ruby language and debugging support for ...
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    Everything you need for PHP development
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    Go to the Marketplace or use our integrated extension manager and search for your desired programming language to find snippets, code completion/IntelliSense providers, linters, debuggers and more.

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    - - - - - - - Markdown - - Python - - T-SQL - TypeScript .

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    Article Text

    Diabetes and endocrinology
    Research
    Safety and efficacy of insulin glargine 300 u/mL compared with other basal insulin therapies in patients with type 2 diabetes mellitus: a network meta-analysis

    Objective To compare the efficacy and safety of a concentrated formulation of insulin glargine (Gla-300) with other basal insulin therapies in patients with type 2 diabetes mellitus (T2DM).

    Design This was a network meta-analysis (NMA) of randomised clinical trials of basal insulin therapy in T2DM identified via a systematic literature review of Cochrane library databases, MEDLINE and MEDLINE In-Process, EMBASE and PsycINFO.

    Outcome measures Changes in HbA1c (%) and body weight, and rates of nocturnal and documented symptomatic hypoglycaemia were assessed.

    Results 41 studies were included; 25 studies comprised the main analysis population: patients on basal insulin-supported oral therapy (BOT). Change in glycated haemoglobin (HbA1c) was comparable between Gla-300 and detemir (difference: −0.08; 95% credible interval (CrI): −0.40 to 0.24), neutral protamine Hagedorn (NPH; 0.01; −0.28 to 0.32), degludec (−0.12; −0.42 to 0.20) and premixed insulin (0.26; −0.04 to 0.58). Change in body weight was comparable between Gla-300 and detemir (0.69; −0.31 to 1.71), NPH (−0.76; −1.75 to 0.21) and degludec (−0.63; −1.63 to 0.35), but significantly lower compared with premixed insulin (−1.83; −2.85 to −0.75). Gla-300 was associated with a significantly lower nocturnal hypoglycaemia rate versus NPH (risk ratio: 0.18; 95% CrI: 0.05 to 0.55) and premixed insulin (0.36; 0.14 to 0.94); no significant differences were noted in Gla-300 versus detemir (0.52; 0.19 to 1.36) and degludec (0.66; 0.28 to 1.50). Differences in documented symptomatic hypoglycaemia rates of Gla-300 versus detemir (0.63; 0.19to 2.00), NPH (0.66; 0.27 to 1.49) and degludec (0.55; 0.23 to 1.34) were not significant. Extensive sensitivity analyses supported the robustness of these findings.

    Conclusions NMA comparisons are useful in the absence of direct randomised controlled data. This NMA suggests that Gla-300 is also associated with a significantly lower risk of nocturnal hypoglycaemia compared with NPH and premixed insulin, with glycaemic control comparable to available basal insulin comparators.

    This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

    http://dx.doi.org/10.1136/bmjopen-2015-009421

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    Strengths and limitations of this study

    This is the first comprehensive literature review and network meta-analysis (NMA) summarising the available clinical trial literature on the clinical benefits of the newly approved basal insulin, Gla-300, and potential basal insulin comparators, and enabling comparisons between these therapies.

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